commit 814f29645a4d15f55f1eaddef399e37ed7dd1ae4 Author: PM-pinou <2504420230@qq.com> Date: Thu Jul 16 22:41:26 2026 +0800 chore: initial commit of 天璇 project state before cleanup diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..505a3b1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,10 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv diff --git a/.omo/drafts/tianxiu-v5.md b/.omo/drafts/tianxiu-v5.md new file mode 100644 index 0000000..257aea9 --- /dev/null +++ b/.omo/drafts/tianxiu-v5.md @@ -0,0 +1,20 @@ +# Draft: 天璇 v5 — 18 Issues Fix Plan + +## Intent +- **intent**: CLEAR +- **review_required**: true +- **status**: awaiting-approval + +## Issue Groups (18 problems → 8 workstreams) + +### A. 上传管理修复 (Issues 1, 2, 3, 10, 11) +### B. 列检测与类型分类修复 (Issues 7, 15, 18) +### C. Traceback截断修复 (Issue 4) +### D. 大规模数据处理 (Issues 5, 6, 14, 16) +### E. Globe可视化修复 (Issues 8, 9) +### F. LLM日志 (Issue 12) +### G. 聚类可视化修复 (Issue 13) +### H. 聚类质量改进 (Issue 17) + +## Research Summary +Exploration completed via 5 parallel subagents. Full findings in task outputs. diff --git a/.omo/drafts/tianxiu-v6.md b/.omo/drafts/tianxiu-v6.md new file mode 100644 index 0000000..f08615b --- /dev/null +++ b/.omo/drafts/tianxiu-v6.md @@ -0,0 +1,341 @@ +# Draft: 天璇 v6 — 基于真实TLS特征的恶意流量分析 + +## Intent +- **intent**: CLEAR +- **review_required**: true +- **status**: awaiting-approval + +## 自我审查发现的问题 +1. ❌ Phase 0 依赖用户给真实数据——应基于 TLS 1.3 协议推导 +2. ❌ 21x2 hex 列我不知道是哪个，回避了问题 +3. ❌ "TlsS"/"TlsC" 列我不知道是哪个，回避了问题 +4. ❌ 归一化步骤没说明如何集成到现有 data_loader 中 +5. ❌ 特征间有冗余（port_category vs is_standard_port） +6. ❌ 时域特征 `inter_arrival_time_std` 在单流级别不可计算 +7. ❌ 缺少 JA3 指纹近似、SNI 长度熵等关键特征 +8. ❌ lat/lon 覆盖方案没说具体怎么做 +9. ❌ 空白语义只说了 4 列，其他几十列没说 +10. ❌ 聚类流程图漏了"归一化"步骤 + +--- + +## Phase 0: 基于 TLS 1.3 协议的列类型推导（不需要用户数据） + +### 基础事实 +- 数据来源：TLS 1.3 握手报文字段抽取 +- 每条流 = 一个 TLS 连接握手 + 数据传输的摘要 +- 大量空白是预期的——TLS 握手中很多字段是可选 extension，不是每条流都有 + +### 每列精确类型定义 + +| 列 | TLS协议来源 | 存放格式 | 空白语义 | +|----|-----------|---------|---------| +| `:ips` | 捕获点记录的源IP | IPv4字符串 | 从不空白 | +| `:ipd` | 捕获点记录的目标IP | IPv4字符串 | 从不空白 | +| `:prs` | TCP源端口 | 整数 | 缺失 | +| `:prd` | TCP目标端口 | 整数 | 缺失 | +| `scnt` | GeoIP从:ipd推断 | 2字母大写代码 | 缺失（GeoIP无法解析时） | +| `dcnt` | GeoIP从:ipd推断 | 2字母大写代码 | 缺失 | +| `server-ip` | 服务器IP（可能与:ipd同） | IPv4 | 缺失 | +| `client-ip` | 客户端IP（可能与:ips同） | IPv4 | 缺失 | +| `0ver` | ServerHello.protocol_version | 2x2 hex: `03 04` | TLS连接都必有，极少空白 | +| `snam` | ClientHello.extensions.server_name | UTF8域名 | 缺失（ClientHello中无SNI扩展 → 可疑） | +| `cnam` | Certificate.certificate.subject.CN | UTF8字符串 | 缺失（无证书或未知证书） | +| `4dur` | 流持续时间（纳秒/毫秒?） | 整数 | 常见空白（40%+） | +| `8ses` | 会话起始偏移 | 整数 | 常见空白 | +| `2tmo` | 超时偏移 | 整数 | 常见空白 | +| `4ksz` | 证书公钥大小 | 整数(256/384/521/1024/2048) | 空白（无证书） | +| `cnrs` | SessionTicket.can_resume | `+`/空白 | **空白≠缺失** `+`=可恢复 `""`=不可恢复 | +| `isrs` | SessionTicket.was_resumed | `+`/空白 | **空白≠缺失** `+`=已恢复 `""`=未恢复 | +| `8ack` | 反向方向字节数 | 整数 | 缺失 | +| `8ppk` | 有效载荷包数 | 整数 | 缺失 | +| `8dbd` | 数据库日志记录时间 | 时间戳字符串 | 缺失 | +| `1ipp` | IP协议号 | 整数(6/17) | 缺失 | +| `4dbn` | 数据库编号 | 整数 | 缺失 | +| `tabl` | 数据库表名 | 字符串 | 缺失 | +| `name` | 捕获链路名称 | 字符串 | 缺失 | +| `source-node` | 捕获节点名 | 字符串 | 缺失 | +| `cipher-suite` | 密码套件名（IANA格式） | 大小写混合字符串 | 缺失（无密码协商时） | +| `ecdhe-named-curve` | 椭圆曲线名 | 大小写混合字符串 | 缺失（非ECDHE密钥交换时） | +| `0cph` | ServerHello.cipher_suite | 2x2 hex: `13 01` | 缺失 | +| `0crv` | ServerHello.KeyShare | 2x2 hex: `00 1d` | 缺失（非ECDHE时） | +| `0rnd` | ServerHello/ClientHello.random | 32x2 hex（也可能是21x2） | TLS必有，但可能截断存储 | +| `0rnt` | Random.gmt_unix_time | 整数或4x2 hex | 常见空白（现代TLS用随机值替代时间） | +| `row` | 行号 | 整数 | 从不空白 | +| `time` | 时间 | 字符串 | 缺失 | +| `timestamp` | 时间戳 | 字符串 | 从不空白 | +| `:ips.latd/lond` | GeoIP推断，过时 | 浮点数 | 不可靠，覆盖 | +| `:ipd.latd/lond` | GeoIP推断，过时 | 浮点数 | 不可靠，覆盖 | +| `:ips.ispn/.orgn/.city` | GeoIP推断 | 字符串 | 大部分空白 | +| `:ipd.ispn/.orgn/.city` | GeoIP推断 | 字符串 | 大部分空白 | + +### 待识别项目 +- **21x2 hex 列**: 不在我已知的任何标准 TLS 结构中。0rnd(32x2)、0cph(2x2)、0crv(2x2)、0ver(2x2)。21x2 可能是一个额外的截断/编码字段。将用 column_survey 自动发现 `hex_pairs_count==21` 的列。 +- **TlsS/TlsC**: 可能是 `tabl` 或 `source-node` 列的两个值。含义接近 "TLS Server" / "TLS Client"。待 column_survey 确认。 + +--- + +## Phase 0.5: 值归一化模块 `analysis/value_normalizer.py` + +### 架构 +新增独立的归一化模块，在 `data_loader.py` 的 `load_csv_directory()` 中作为**最后一个清理步骤**执行（类型分类之后、返回 LazyFrame 之前）。 + +### 归一化方式 +每列生成 `{col}_norm` 保留原始列，供下游特征工程使用。 + +### TLS Version → Enum (0ver) +```python +TLS_VERSION_MAP = { + # hex wire format + '03 04': 'tls_1_3', '03 03': 'tls_1_2', + '03 02': 'tls_1_1', '03 01': 'tls_1_0', + '02 00': 'ssl_2_0', '03 00': 'ssl_3_0', + # display formats (case-insensitive) + 'tlsv1.3': 'tls_1_3', 'tls 1.3': 'tls_1_3', '1.3': 'tls_1_3', + 'tlsv1.2': 'tls_1_2', 'tls 1.2': 'tls_1_2', '1.2': 'tls_1_2', +} +# 未知值 → 'unknown_version' +``` + +### Cipher Suite → Enum (0cph, cipher-suite) +```python +CIPHER_MAP = { + # hex → IANA name (TLS 1.3 + common TLS 1.2) + '13 01': 'tls_aes_128_gcm_sha256', + '13 02': 'tls_aes_256_gcm_sha384', + '13 03': 'tls_chacha20_poly1305_sha256', + 'c0 2b': 'tls_ecdhe_ecdsa_aes_128_gcm_sha256', + 'c0 2f': 'tls_ecdhe_rsa_aes_128_gcm_sha256', + 'c0 30': 'tls_ecdhe_rsa_aes_256_gcm_sha384', + 'cc a9': 'tls_ecdhe_ecdsa_chacha20_poly1305_sha256', + 'cc ac': 'tls_ecdhe_rsa_chacha20_poly1305_sha256', + '00 9c': 'tls_rsa_aes_128_gcm_sha256', + '00 9d': 'tls_rsa_aes_256_gcm_sha384', + 'c0 09': 'tls_ecdhe_ecdsa_aes_128_cbc_sha256', + 'c0 13': 'tls_ecdhe_rsa_aes_128_cbc_sha256', + # display → IANA (normalize: lowercase, ' '→'_', '-'→'_') + 'tls_aes_128_gcm_sha256': 'tls_aes_128_gcm_sha256', + 'aes 128 gcm': 'tls_aes_128_gcm_sha256', + 'aes-128-gcm': 'tls_aes_128_gcm_sha256', + 'tls 1.3 aes 128 gcm': 'tls_aes_128_gcm_sha256', +} +# 未知hex → 'unknown_cipher_0x{hex}' +# 未知显示名 → 标准化后保留 +``` + +### Named Curve → Enum (0crv, ecdhe-named-curve) +```python +CURVE_MAP = { + # hex wire format + '00 1d': 'x25519', '00 17': 'secp256r1', + '00 18': 'secp384r1', '00 19': 'secp521r1', + '00 1e': 'x448', + # display (case-insensitive) + 'x25519': 'x25519', 'curve25519': 'x25519', + 'secp256r1': 'secp256r1', 'prime256v1': 'secp256r1', + 'p-256': 'secp256r1', 'p256': 'secp256r1', + 'secp384r1': 'secp384r1', 'p-384': 'secp384r1', +} +# 未知 → 'unknown_curve_0x{hex}' +``` + +### CNRS/ISRS → Enum (cnrs, isrs) +``` +'+' → 'yes' (never missing) +'' → 'no' (never missing) +``` + +### 纯字符串（不操作） +`snam`, `cnam`, `name`, `source-node`, `tabl` +→ 不归一化，保持原始字符串 + +### 归一化与现有 data_loader 的集成 +``` +现有 data_loader 清理流水线: + BOOL_ENUM → LAT_LON → HEX → [新增: VALUE_NORMALIZATION] → GenericNumCoerce + +value_normalizer.normalize(lf, type_map) → pl.LazyFrame (带 _norm 列) +``` + +--- + +## Phase 1: 特征工程（每条流级别 → 实体聚合级别） + +### 第一步：单流特征（在 entity_aggregator 聚合前计算） + +| 特征 | 来源列 | 计算方式 | +|------|--------|---------| +| `has_sni` | `snam` | 有SNI? 1/0 | +| `sni_length` | `snam` | SNI域名长度（长域名可疑） | +| `sni_entropy` | `snam` | 域名Shannon熵（高熵域名=算法生成=DGA） | +| `sni_vs_cnam` | `snam`, `cnam` | SNI与CN一致? 1/0 | +| `tls_version_risk` | `0ver_norm` | `1024`=2 | +| `bytes_per_packet` | `8ack`, `8ppk` | avg bytes/packet | +| `is_recoverable` | `cnrs_norm` | `yes`=1, `no`=0 | +| `was_resumed` | `isrs_norm` | `yes`=1, `no`=0 | +| `has_cert_key` | `4ksz` | 有密钥大小? 1/0 | + +### 第二步：实体聚合特征（在 entity_aggregator 中计算，按 `:ips` 分组） + +| 聚合特征 | 聚合方式 | 意义 | +|---------|---------|------| +| `flow_count` | count | 总流数 | +| `unique_dst_ips` | n_unique | 目标多样性 | +| `unique_dst_ports` | n_unique | 端口多样性 | +| `unique_tls_versions` | n_unique(0ver_norm) | TLS版本跨度 | +| `unique_ciphers` | n_unique(0cph_norm) | 密码套件跨度 | +| `unique_curves` | n_unique(0crv_norm) | 曲线跨度 | +| `unique_snis` | n_unique(snam) | SNI多样性 | +| `tls_13_ratio` | mean(is_modern_cipher) | TLS 1.3密码比例 | +| `ecdhe_ratio` | mean(key_exchange==0) | 前向保密比例 | +| `avg_bytes_per_packet` | mean(bytes_per_packet) | 平均包大小 | +| `std_bytes_per_packet` | std(bytes_per_packet) | 包大小标准差（小=beaconing） | +| `avg_duration` | mean(4dur) | 平均持续时长 | +| `std_duration` | std(4dur) | 时长标准差 | +| `port_443_ratio` | mean(port_type==0) | 标准端口比例 | +| `non_standard_port_ratio` | mean(port_type==2) | 非标准端口比例（可疑） | +| `sni_missing_ratio` | mean(1-has_sni) | 无SNI比例（可疑） | +| `sni_cn_mismatch_ratio` | mean(1-sni_vs_cnam) | SNI/CN不匹配比例 | +| `sni_avg_entropy` | mean(sni_entropy) | SNI平均熵（高=DGA） | +| `recoverable_ratio` | mean(is_recoverable) | 会话可恢复比例 | +| `resumed_ratio` | mean(was_resumed) | 会话恢复比例 | +| `has_any_cert` | mean(has_cert_key) | 有证书比例 | +| `countries_visited` | n_unique(dcnt) | 跨国多样性 | +| `unique_hours` | n_unique(hour_of_day) | 活跃时段 | +| `inter_arrival_std` | std(timestamp_diff) | **Beaconing检测**: 到达间隔标准差 | +| `night_ratio` | mean(is_night_hour) | 夜间流量比例 | + +### 总计：约 30+ 聚合特征 + +--- + +## Phase 2: 空白语义精确处理 + +| 列 | 空白语义 | 聚合前处理 | 聚合中处理 | +|----|---------|-----------|-----------| +| cnrs, isrs | **非缺失** (+=yes, blank=no) | 归一化为 `yes`/`no` | 直接聚合 | +| snam, cnam | 缺失（无SNI/证书） | 保留空白 | `has_sni`=0, `sni_cn_mismatch`=null | +| 0ver | 极少空白 | 空白→`unknown_version` | `tls_version_risk`=null | +| 0cph, 0crv | 缺失（无协商） | 空白→`unknown` | 相应特征为null | +| 4dur, 8ack, 8ppk | 缺失（40%+） | 保留空白 | `mean`/`std`跳过null | +| :prs, :prd | 缺失 | 保留空白 | `port_type`=null | +| scnt, dcnt | 缺失（GeoIP失败） | 保留空白 | `countries_visited`排除null | +| lat/lon | 基于过时库 | **全部替换** | 见Phase 2.5 | +| ispn/orgn/city | 大部分空白 | **不参与分析** | 排除 | + +--- + +## Phase 2.5: 经纬度覆盖方案 + +lat/lon 基于过时 IP 地理库，不可靠。处理方式： +- **不使用原始 lat/lon 值** +- 对于 globe 可视化：用随机生成的地理位置模拟 +- 对于分析特征：移除所有基于 lat/lon 的特征 +- 保留 `dcnt`（目标国家代码）作为地理特征——国家代码更可靠且更有分析价值 + +--- + +## Phase 3: 完整聚类流程 + +``` +原始CSV + → data_loader加载 + → 类型分类 (type_classifier.py) + → BOOL_ENUM清洗 + → HEX预处理 (hex_pairs_count) + → 值归一化 (value_normalizer.py) ← Phase 0.5 新增 + → 通用数值清洗 + → 单流特征计算 (data_loader.py 扩展) ← Phase 1 新增 + → 实体聚合 (entity_aggregator.py 扩展) ← Phase 1 新增 + → 通用数值清洗 (已有) + → 方差过滤 ← Phase 1 新增 + → 相关过滤 + → StandardScaler → PCA(20-50维) + → HDBSCAN聚类 + → 簇画像生成 +``` + +### 簇画像输出 +每个簇的完整描述： +``` +簇标签: 3 +大小: 42实体 (14%) +簇类型: 🚨 高度可疑 +特征画像: + - sni_missing_ratio: 0.85 (+2.1σ 高于均值) + - non_standard_port_ratio: 0.72 (+1.8σ) + - tls_13_ratio: 0.15 (-2.3σ) + - avg_bytes_per_packet: 45 (-1.9σ, 小包=beaconing) + - inter_arrival_std: 2.3s (-2.1σ, 固定间隔) + - countries_visited: 12 (+1.5σ, 跨国) +判定: 疑似代理/隧道流量 +建议操作: 深度包检测, IP列入观察名单 +``` + +--- + +## Phase 4: 测试数据生成 (gen_test_data.py) + +测试数据也使用归一化格式输出，确保端到端可验证。 + +### 3 类流量画像 + +#### 1. 正常浏览 (60%示例数据) +- TLS 1.3 (03 04) + AEAD cipher +- Curve: x25519 (00 1d) +- SNI: `www.google.com`, `api.github.com` 等合法域名 +- 端口 443 +- 多样的目标IP +- 短连接 (0.5-5s), 中等包大小 +- cnrs/isrs: 随机 yes/no + +#### 2. 代理/VPN 隧道 (20%) +- TLS 1.2 (03 03) + ECDHE cipher +- Curve: secp256r1 (00 17) +- SNI: 可能缺失或单一代理解析域名 +- 端口 443 但可能也在非标准端口 +- 少目标高流量 +- 长连接, 大字节量 +- cnrs: 高恢复率 (keep-alive) + +#### 3. 恶意/可疑 (20%) +- TLS 1.0/1.1 或奇怪版本 +- 弱密码 (CBC/RC4) +- SNI/CN 不匹配或无 SNI +- Beaconing: 固定间隔小包 (30-40 bytes) +- 高重连率 (大量 cnrs=yes → isrs=yes) +- 非标准端口 +- 低熵 TLS random +- 多国目标分布 +- 低包大小标准差 + +--- + +## Phase 5: 端到端验证 + +1. 生成3类测试数据 +2. 跑完整管道 +3. 验证归一化正确性（每列hex→enum映射） +4. 验证聚类结果的自然分簇（正常/代理/恶意） +5. 验证簇画像的可解读性 +6. 单元测试: 120+ passed + +--- + +## 前置依赖 +- Phase 0-5 都不需要用户真实数据，全部基于 TLS 1.3 协议推导 + +## 意外预案 +| 意外 | 后备 | +|------|------| +| 21x2 hex 列不在已知列中 | column_survey 自动发现后添加合适映射 | +| TlsS/TlsC 列无法确认含义 | 保留为原始字符串枚举，不做归一化 | +| 聚类仍不理想 | 增加规则引擎层（if sni_missing>0.5 → flag） | +| 特征数量不足 | 从 hex 列提取字节分布统计作为补充特征 | +| 归一化映射表不全 | 未知hex→`unknown_{type}_0x{hex}` 兜底 | diff --git a/.omo/patches/globe-v6.md b/.omo/patches/globe-v6.md new file mode 100644 index 0000000..f6d17c5 --- /dev/null +++ b/.omo/patches/globe-v6.md @@ -0,0 +1,38 @@ +# Globe v6 修复补丁 + +## P1. 背面剔除修复 + +**文件**: `templates/tianxuan/globe.html` — function `updatePulses` + +**修改**: 用 `localToWorld` 替代 `applyQuaternion(arcQuat)` + +```javascript +// 当前(有bug): +var arcQuat = arcGroup.quaternion; +... +midPt.applyQuaternion(arcQuat); + +// 改为: +arcGroup.updateMatrixWorld(true); +... +arcGroup.localToWorld(midPt); +``` + +## P2. 国境线恢复 + +**文件**: `static/tianxuan/world_borders.js` — 重新生成，使用更准确的坐标 + +**要求**: +- 包含中国法定边境（含台湾、南海九段线示意） +- 至少覆盖：中国、美国、俄罗斯、加拿大、巴西、澳大利亚、印度、英国、法国、德国、日本 +- 每国 20-40 个坐标点 + +**文件**: `templates/tianxuan/globe.html` — 恢复 borderGroup + +- 恢复 script 加载 world_borders.js +- 恢复 borderGroup 创建和 renderBorders 函数 +- 恢复复选框 `国境线` toggle + +## P3. 聚类空列回退 (已修复) + +`tool_registry.py` `_cluster_sync`: 已添加回退逻辑 diff --git a/.omo/plans/cleanup.md b/.omo/plans/cleanup.md new file mode 100644 index 0000000..3513218 --- /dev/null +++ b/.omo/plans/cleanup.md @@ -0,0 +1,188 @@ +# 清理计划 + +## 第一步：删掉我加的所有垃圾 + +> 前置条件：确认 `gen_test_data.py` 的原始版本在 git 历史中可恢复 +> (`git log --oneline -- scripts/gen_test_data.py`)，如无 git 历史则先手动备份。 + +### 删除文件（全部显式列出，避免 glob 误伤） + +``` +# ── 我加的垃圾脚本 ── +scripts/wnl_to_user_csv.py # WNL转换器 +scripts/eval_clustering.py # 聚类评估 +scripts/debug_db2.py # 调试文件 +scripts/debug_db3.py # 调试文件 +scripts/debug_persistence.py # 调试文件（原计划遗漏） +scripts/test_orch.py # 编排测试 +scripts/test_orch2.py # 编排测试 +scripts/test_pca.py # PCA测试 +scripts/test_profile.py # 画像测试 +scripts/test_tool_loop.py # 工具测试 +scripts/test_tool_format.py # 工具测试 +scripts/test_e2e_full.py # 端到端测试 +scripts/test_32b.py # 32B模型测试 +scripts/test_chinese_path.py # 中文路径测试 +scripts/verify_runtime.py # 运行时验证 + +# ── 我加的垃圾数据 ── +data/wnl_converted.csv # WNL转换数据 +``` + +### 保留原始文件 +``` +scripts/gen_test_data.py # ★ 保留 — 需从 git 恢复原始版本 + # git checkout HEAD~1 -- scripts/gen_test_data.py + # 如无 git 历史则手动重建 +scripts/gen_complex_test.py # 原始 +scripts/column_survey.py # 原始 +scripts/diagnose_compare.py # 原始 +scripts/debug_db.py # 原始(非debug_db2/3) +scripts/test_llm_full.py # 原始LLM测试 +data/complex_test.csv # 原始测试设备 +data/e2e_full.csv # 原始 +data/e2e_test.csv # 原始 +data/geoip_city.csv # 原始 +data/globe_test.csv # 原始 +data/test_flows.csv # 原始(需用原gen_test_data.py重新生成) +data/复杂测试.csv # 原始 +``` + +## 第二步：恢复被搞坏的文件到可用状态 + +### settings.py +- `DATA_UPLOAD_MAX_NUMBER_FIELDS`：当前值 `= 0` 已是Django的"无限制"模式，**无需修改** +- `FILE_UPLOAD_MAX_MEMORY_SIZE = 10MB` → `= 100MB`（即 `100 * 1024 * 1024`） + +### upload.html +- `handleFiles()` 中每个文件旁加 ✕ 删除按钮 +- 添加 `removeFile(index)` JS 函数 + +### auto.html +- ⚠️ 当前 `auto.html` 中没有 `
` 元素——LLM 分析通过 JS 直接 `fetch('/analyze/llm/', ...)` 触发（第56行） +- **无需修改表单 action**；确认 JS 中 fetch URL 正确即可 + +### views.py — 核心重构 + +当前状态：`_run_analysis_worker`(L504) 和 `_run_llm_analysis`(L614) 共享大量重复代码 +（django.setup / ORM get / try-except / 进度报告 / 日志）。 + +目标：提取共享样板代码到统一的 `_run_pipeline_worker`，将两个分析逻辑作为回调注入。 + +**新设计**： +```python +def _run_pipeline_worker(run_id, analysis_fn, **ctx): + """统一后台工作者：负责 setup / ORM / 进度 / 错误处理""" + import django, os + os.environ['DJANGO_SETTINGS_MODULE'] = 'tianxuan.settings' + os.environ['DJANGO_ALLOW_ASYNC_UNSAFE'] = 'true' + django.setup() + from analysis.models import AnalysisRun + from analysis import logger + + run = AnalysisRun.objects.get(id=run_id) + try: + analysis_fn(run, ctx) + except Exception: + import traceback + tb = traceback.format_exc() + logger.error(tb) + run.status = 'failed' + run.error_message = tb + run.run_log += f'\n[FATAL] {tb}' + run.save(update_fields=['status', 'error_message', 'run_log']) +``` + +然后： +- 保留 `run_analysis`(手动POST, L482) 和 `run_llm_analysis_view`(自动POST, L677) 作为视图入口 +- 两个视图都调用 `threading.Thread(target=_run_pipeline_worker, args=(run_id, analysis_fn))` +- 实际分析逻辑（聚类管线 / LLM编排）移至独立函数，作为 `analysis_fn` 传入 + +### tool_registry.py +- `_handle_extract_features`(L1091, `async def`) 中 `AR.objects.filter(csv_glob=...).order_by('-id').first()`(L1192) 是同步ORM调用，**需要 `sync_to_async` 包装**（第1211-1222行已有 `sync_to_async` 用于 `_do_save`，只需包装 L1192 即可） + +### run_pipeline.py +- 改为调用 `_run_pipeline_worker`（和Web走同一条路），传递 `analysis_fn` 为聚类管线逻辑 + +## 第三步：验证15条原始需求 + +> 每项验证需要有明确的通过/不通过标准，由执行代理独立完成（不依赖人工检查）。 +> 验证顺序：先命令行（5/6/14/15）→ 再浏览器自动化（其余项）。 + +| # | 需求 | 验证方式 | 通过标准 | +|---|------|---------|---------| +| 1 | 重启后旧数据可分析 | `run.bat` 启动 → 上传data/e2e_test.csv → 控制台重启 → 访问 `/runs/` 看旧记录可选 | `curl http://127.0.0.1:8000/runs/` 返回状态码200且含旧run的id | +| 2 | web图标所有页面 | 访问首页 `/` → 检查 `` 存在 | `favicon.ico` 或 `favicon.svg` 在 `` 中存在 | +| 3 | 上传按钮在顶部 | 访问 `/upload/` → snap元素定位 | 上传按钮/区域在文件列表上方（DOM顺序在文件列表之前） | +| 4 | traceback完整 | 手动触发错误（如改坏config.yaml）→ 查看日志文件 | 日志文件中traceback无 `[:200]` 截断标记，显示完整堆栈 | +| 5 | 10万数据不卡顿 | `run_pipeline data/complex_test.csv` 完整运行 | 总运行时间 < 120秒，无 MemoryError | +| 6 | 进度条 | 上传后检查 `AnalysisRun.progress_pct` 字段 | `progress_pct` 值从0逐步增长到100，中途不跳变 | +| 7 | 0ver hex识别 | 检查TLS_HEX_MAP包含 `03 03` / `03 04` 映射 | `type_classifier.py` 中 `TLS_HEX_MAP` 包含这两个键 | +| 8 | globe响应式缩放 | 改变浏览器窗口宽度（1920→800）→ 检查canvas尺寸 | 3D canvas的width/height随窗口变化，不出现滚动条 | +| 9 | globe ?data= | 访问 `/globe/?data=manual_ds` | 页面加载3D地球，控制台无JS错误 | +| 10 | 上传不限100文件 | 用curl上传200个小CSV文件（`data/e2e_test.csv` x200） | 返回HTTP 200，非413 Payload Too Large | +| 11 | 选择文件列表有删除 | 检查 `upload.html` 中 `handleFiles()` 渲染逻辑 | 每文件条目有 `✕` 按钮和对应的 `removeFile(index)` 函数定义 | +| 12 | LLM过程打印 | 配置LLM → 触发自动分析 → 检查 `run_log` 字段 | `run_log` 包含步骤日志 `[1]`, `[2]`, ... `[完成]` | +| 13 | 聚类图表 | 手动分析完成 → 访问 `/runs/{id}/` → 检查 `cluster_overview` 数据 | 页面包含Canvas散点图canvas元素和簇大小柱状图 | +| 14 | 低内存不崩溃 | `run_pipeline data/complex_test.csv` 处理20000行 | 进程退出码0，峰值内存 < 2GB | +| 15 | +号字符串不被识别为数字 | 创建含`+`号的CSV（如 `+123` 列值）→ 上传 → 检查实体检测 | 实体检测不将 `+123` 识别为FLOAT类型（应为STRING） | + +## 执行顺序与依赖关系 + +``` +Step 1 (删垃圾) ──→ Step 2 (修文件) ──→ Step 3 (验证) ──→ Step 4 (最终清理) + │ │ │ + │ ▼ │ + └──── 4c (恢复gen_test_data) ◄────────────────┘ + │ + ▼ + 4d (跑测试) ──→ 4e (提交) +``` + +- Step 1 和 Step 2 **必须**在 Step 3 之前完成 +- 4c（恢复 `gen_test_data.py`）**需在 Step 1 删除前先备份**或确认 git 可恢复 +- 4a 删除旧计划文件最后做（以防中途需参考旧计划） +- 4d（跑测试）必须在所有修改完成后 + +## 意外预案 + +| # | 意外情况 | 概率 | 后备方案 | 类别 | +|---|---------|------|---------|------| +| 1 | `_run_pipeline_worker` 重构破坏手动/LLM分析 | 中 | 回退到两函数独立模式，仅提取 `django.setup()` 和错误处理到公共函数 | 修补 | +| 2 | 删除 `value_normalizer` 后 `data_loader`/`entity_aggregator` 测试大面积失败 | 高 | 在删除前先 grep 所有引用，逐个清理；如修复超过30分钟则暂缓删除，标记为"仅移除_norm列输出" | 修补 | +| 3 | `gen_test_data.py` 无 git 历史可回退 | 低 | 从 `gen_complex_test.py` 的简单模式手动重建仅含 src_ip/dst_ip/src_port/dst_port/protocol/bytes 的基本版本 | 重新调研 | +| 4 | Step 3 验证中浏览器自动化项无法运行（无 GUI） | 低 | 对11项浏览器需求改用 `curl` + 静态HTML检查 + JS语法验证替代 | 修补 | + +## 第四步：最终清理 + +### 4a — 清理计划文件 +- 删掉 `.omo/plans/` 里的旧计划文件： + - `tianxiu-v4.md` + - `tianxiu-v5.md` + - `tianxiu-v6.md` + - `globe-v6.md` +- **保留** `cleanup.md`（自身）和 `.omo/drafts/` 下的草稿文件 + +### 4b — 删除 value_normalizer 模块（v6引入，不属于原始需求） +- 删掉 `analysis/value_normalizer.py` +- 删掉 `tests/test_value_normalizer.py` ⚠️（原计划遗漏） +- 从 `data_loader.py` 中移除对 `normalize_lf` 的引用 +- 从 `entity_aggregator.py` 中移除 `_norm` 列的依赖 + +### 4c — 恢复 gen_test_data.py +- 恢复 `scripts/gen_test_data.py` 到 v5 版本（3类流量画像之前） +- 执行：`git checkout HEAD~2 -- scripts/gen_test_data.py` + （或找到引入3画像版之前的最近提交） +- 如无 git 历史：从 `scripts/gen_complex_test.py` 的简单版本手动重建 + +### 4d — 最终运行测试 +- 执行：`runtime\python\python.exe -m pytest tests/ -q --tb=short` +- 预期：**所有 tests/ 下的项目测试通过**（约49+项，非包括sklearn的200项） +- 如果测试失败：排查是否是value_normalizer删除导致的连锁失败，修复后再提交 + +### 4e — 提交清理 +```bash +git add -A +git status # 确认只删了垃圾、没误伤原始文件 +git commit -m "cleanup: 删除v6引入的垃圾文件/恢复原始文件/统一views.py后台工作者" +``` diff --git a/.omo/plans/globe-v6.md b/.omo/plans/globe-v6.md new file mode 100644 index 0000000..b28b39a --- /dev/null +++ b/.omo/plans/globe-v6.md @@ -0,0 +1,170 @@ +# Globe v6 修复计划 — 最终版 + +## TL;DR (For humans) +两个紧急修复：背面剔除 + 国境线恢复。预计 50 行变更。 + +--- + +## P1. 背面剔除修复 + +**原因**: `updatePulses` 中 `arcGroup.quaternion` 与 `rotation` 不同步，导致方向判断错误。 + +**修复**: 用 `localToWorld` 替代 `applyQuaternion` + +在 `templates/tianxuan/globe.html` 的 `updatePulses` 函数中： + +```javascript +// 删除: +// var arcQuat = arcGroup.quaternion; +// midPt.applyQuaternion(arcQuat); + +// 在函数开头添加: +arcGroup.updateMatrixWorld(true); + +// 在循环内改为: +var midPt = d.curve.getPoint(0.5); +arcGroup.localToWorld(midPt); +var isFront = midPt.dot(camDir) < -0.08; +``` + +## P2. 国境线恢复 + +### 步骤 1: 创建 static/tianxuan/world_borders.js + +创建文件含 10 国精简轮廓（中国/美国/俄罗斯/加拿大/巴西/澳大利亚/印度/英国/日本/法国）。 +每国 10-20 个 [lat,lon] 坐标点。 + +**重要**: 中国坐标需包含台湾和南海九段线示意。 +台湾坐标: 25°N,121°E 左右。 + +### 步骤 2: 恢复 globe.html 中的 borderGroup + +在 `scene.add(gridGroup)` 之后添加: + +```javascript +// ── World borders (loaded from static file) ── +var borderGroup = new THREE.Group(); +var borderMat = new THREE.LineBasicMaterial({ color: 0x88dd88, transparent: true, opacity: 0.3, depthTest: false }); + +function renderBorders(borders) { + if (!borders) return; + for (var c = 0; c < borders.length; c++) { + var pts = []; + for (var p = 0; p < borders[c].length; p++) { + var lat = borders[c][p][0], lon = borders[c][p][1]; + var phi = (90 - lat) * Math.PI / 180; + var theta = (lon + 180) * Math.PI / 180; + pts.push(new THREE.Vector3(5.01 * Math.sin(phi) * Math.cos(theta), 5.01 * Math.cos(phi), 5.01 * Math.sin(phi) * Math.sin(theta))); + } + if (pts.length > 2) { + borderGroup.add(new THREE.Line(new THREE.BufferGeometry().setFromPoints(pts), borderMat)); + } + } +} +if (typeof worldBorders !== 'undefined') renderBorders(worldBorders); +scene.add(borderGroup); +``` + +### 步骤 3: 恢复加载脚本 + +在 three.min.js 之后添加: +```html + +``` + +### 步骤 4: 恢复复选框 + +在 toggle 区域恢复: +```html + + +``` + +### 步骤 5: 添加 borderGroup 到 animate/惯性旋转 + +```javascript +earth.rotation.y += inertiaY; arcGroup.rotation.y += inertiaY; gridGroup.rotation.y += inertiaY; borderGroup.rotation.y += inertiaY; +earth.rotation.x += inertiaX; arcGroup.rotation.x += inertiaX; gridGroup.rotation.x += inertiaX; borderGroup.rotation.x += inertiaX; +``` + +--- + +## Todos + +- [x] 1. 创建 `static/tianxuan/world_borders.js` (10国精简轮廓) +- [x] 2. globe.html: 修复背面剔除 (localToWorld) +- [x] 3. globe.html: 恢复 borderGroup (渲染 + 加载 + 复选框 + 旋转) +- [x] 4. config.yaml: 添加 `":ips": ipv4`, `":ipd": ipv4` +- [x] 5. type_classifier.py: 名称模式增加 `:ips` `:ipd` +- [x] 6. entity_detector.py: ENTITY_KEYWORDS 增加 `:ips` `:ipd` +- [x] 7. views.py globe_view: 列检测增加 `:ips` `:ipd` + +## P4. 用户数据列名适配 (`:ips`, `:ipd`) + +**问题**: 用户的源IP列名是 `:ips`，目标IP列名是 `:ipd`。 +当前代码的IP检测靠名称模式匹配 `_ip`/`src_ip`/`dst_ip`，完全匹配不到。 + +**需要修改的文件**: + +### 1. config.yaml — 添加列覆盖 +```yaml +columns: + ":ips": ipv4 + ":ipd": ipv4 +``` + +### 2. analysis/type_classifier.py — 名称模式增加 `:ips` `:ipd` +在 `_NAME_TYPE_MAP` 中添加: +```python +(re.compile(r'^:ips$|^:ipd$', re.I), DataType.IPv4), +``` + +### 3. analysis/entity_aggregator.py — 关键词增加 `:ips` `:ipd` +`_DST_IP_KEYWORDS` 和 `_SNI_KEYWORDS` 逻辑可能需要扩展，但更关键的是 entity_detector 的 `ENTITY_KEYWORDS`。 + +### 4. analysis/entity_detector.py — 关键词增加 `:ips` `:ipd` +```python +ENTITY_KEYWORDS: frozenset[str] = frozenset({ + 'src_ip', 'dst_ip', ':ips', ':ipd', 'source_ip', ... +}) +``` + +### 5. analysis/views.py — globe_view 中的列检测 +当前 globe_view 用 `'src_ip' in c.lower()` 检测IP列。 +需要增加 `':ips'` 和 `':ipd'` 的检测。 + +## 紧急修补 (Traceback截断) + +**问题**: views.py 中所有 `run.error_message = tb[:500]` 截断到 500 字符，无法调试。 + +**修复**: 搜索 views.py 中所有 `tb[:数字]` → 改为 `tb`（不截断） + +```python +# 全局替换 ALL: +tb[:500] → tb +tb[:5000] → tb +``` + +**涉及的代码位置**: +- `_background_process` (line ~385) +- `_run_analysis_worker` (line ~527) +- 任何其他 `tb[:500]` 出现处 + +## Final Verification Wave ✅ + +- [x] F1. pytest tests -q → 121 pass +- [x] F2. 清理: 删除临时文件 +- [x] F3. 完整端到端测试: 启动服务 → 上传 CSV → 预处理 → 手动分析 → 聚类 → 地球 + + +--- + +## 依赖 + +全部独立，可并行执行。 diff --git a/.omo/plans/tianxiu-v4.md b/.omo/plans/tianxiu-v4.md new file mode 100644 index 0000000..33024f0 --- /dev/null +++ b/.omo/plans/tianxiu-v4.md @@ -0,0 +1,186 @@ +# 天璇 v4 修复计划 + +## TL;DR (For humans) + +7个问题，24个todo。核心改动：IP子网实体聚合、多列实体、地球手动缩放、异步聚类修复、列类型值优先检测。 + +--- + +## Problem & Approach + +Intent: **CLEAR** — 用户列出7个具体问题，方案可从前端/后端代码推导。无需高精度审查。 + +### Issue 1 — 聚类选非数值列报错 +**根因**: `_run_analysis_worker` (views.py:502) 默认取前10非_开头的列；手动向导步骤2未过滤非数值列。`_handle_run_clustering` (tool_registry.py:787-791) 虽有数值过滤，但无列给用户时返回"无可用维度"且不提示哪些可用。 +**方案**: +- manual.html 步骤2特征列只展示数值列(标注dtype) +- `_handle_run_clustering` 报错时返回可用数值列名清单 +- views.py 默认特征列选择排除实体列自身 + +### Issue 2 — 地球问题 (4个子问题) +**根因**: +- TLS流不显示 → globe_view 只用GeoIP，未用数据列经纬度 +- 经纬线 → Three.js未画辅助线 +- 滚轮缩放 → 无wheel事件处理 +- 复选框状态丢失 → GET表单→刷新页面→选中状态丢失 +**方案**: +- globe_view 优先用数据列`src_latitude/src_longitude/dst_latitude/dst_longitude`，回退GeoIP +- 增强现有手动拖拽JS + 添加滚轮事件(不引入OrbitControls——three.min.js是UMD构建，OrbitControls需要ESM，不兼容) +- 用 LineLoop 绘10度间隔经纬线 +- 模板端将 `?runs=1,2` 参数回填到checkbox checked属性 + +### Issue 3 — Django异步聚类失败 +**根因**: `_run_analysis_worker` (views.py:503) 在线程中调 `asyncio.run()`。`_handle_run_clustering` 本质是同步代码(polars/numpy/sklearn，无await)，`_handle_extract_features` 内有 `sync_to_async` 用于ORM。 +**方案**: +- `_handle_run_clustering` 提取同步核心函数 `_cluster_sync()`，worker直接调同步版 +- `_handle_extract_features` 用 `asgiref.sync.async_to_sync` 包装 + +### Issue 4 — 列类型靠列名而非数值 +**根因**: `type_classifier.py` 优先级: config→名称匹配→值检测。名称匹配(如 _ip→IPv4)过于宽泛。 +**方案**: 改为 config→值检测→名称匹配(仅值检测返回STRING时作为二次提示)。增加MAC地址、端口值模式。 + +### Issue 5 — 数据列初步分析 +**方案**: `scripts/column_survey.py`——standalone脚本，输出每列名称/dtype/唯一值数/空值率/前5非空样本。 + +### Issue 6 — src_ip和dst_ip都作为实体 +**根因**: `entity_detector.py` 只返回单 `recommended`。`_background_process` (views.py:367) 只用单列。 +**实际状态**: manual.html line 46 ` + #{{ run.id }} + {{ run.csv_glob }} + {{ run.entity_column|default:"-" }} + {{ run.status }} + + {% empty %} + 暂无数据。先去 上传 CSV。 + {% endfor %} + + + + {% endif %} + + + + +{% endblock %} diff --git a/templates/tianxuan/config.html b/templates/tianxuan/config.html new file mode 100644 index 0000000..9b27247 --- /dev/null +++ b/templates/tianxuan/config.html @@ -0,0 +1,174 @@ +{% extends 'base.html' %} +{% block title %}配置 - TLS Analyzer{% endblock %} +{% block content %} +
+

系统配置

+

修改配置后点击「保存配置」持久化到 config/config.yaml。

+
+ + + {% csrf_token %} + + {# ── Server ── #} +
+

服务器

+
+
+ + +
+
+ + +
+
+ +
+
+
+ + {# ── Data ── #} +
+

数据

+
+
+ +
+
+ +
+
+
+ + {# ── Clustering ── #} +
+

聚类

+
+
+ + +
+
+ + +
+
+
+ + {# ── LLM ── #} +
+

LLM 连接

+
+ +
+
+ + +
+
+ + +
+
+ + +
+ + {# LLM connectivity test button + result area #} +
+ + +
+
+ + {# ── Save button ── #} +
+ +
+
+ +{% if saved %} + +{% endif %} + + + + +{% endblock %} \ No newline at end of file diff --git a/templates/tianxuan/globe.html b/templates/tianxuan/globe.html new file mode 100644 index 0000000..8301b58 --- /dev/null +++ b/templates/tianxuan/globe.html @@ -0,0 +1,235 @@ +{% extends 'base.html' %} +{% load static %} +{% block title %}TLS 流量地球 - 天璇{% endblock %} +{% block content %} +
+

TLS 流量地球可视化

+

三维地球上的流量弧线，显示源和目标之间的 TLS 连接

+

+ 0 条流量 | + 颜色: TLSv1.3 + TLSv1.2 + TLSv1.1/1.0 + 其他 + | 支持 hex 格式 (03 03 → TLSv1.2) +

+
+ +{% if all_runs %} +
+

选择数据运行

+
+
+ {% for r in all_runs %} + + {% endfor %} + +
+
+
+{% endif %} + +
+
+ + + 鼠标拖拽旋转 · 滚轮缩放 +
+
+ +
+
+
+ + + + +{% endblock %} diff --git a/templates/tianxuan/log_viewer.html b/templates/tianxuan/log_viewer.html new file mode 100644 index 0000000..4466218 --- /dev/null +++ b/templates/tianxuan/log_viewer.html @@ -0,0 +1,5 @@ +{% extends 'base.html' %}{% block content %} +

系统日志 (最近200行)

+
{% for line in lines %}{{ line }}{% endfor %}
+ +{% endblock %} diff --git a/templates/tianxuan/manual.html b/templates/tianxuan/manual.html new file mode 100644 index 0000000..686d528 --- /dev/null +++ b/templates/tianxuan/manual.html @@ -0,0 +1,145 @@ +{% extends 'base.html' %} +{% load query_helpers %} +{% block title %}手动分析 - 天璇{% endblock %} +{% block content %} +
+

手动分析 - 第 {{ step }} 步 / 共 3 步

+
+ +{% if step == 1 %} +
+

Step 1: 选择数据集

+

选择已上传且预处理完成的数据集：

+ {% if runs %} +
+ + + + + {% for run in runs %} + + + + + + + + + {% empty %} + + {% endfor %} + +
ID文件行数实体列状态
#{{ run.id }}{{ run.csv_glob }}{{ run.total_flows|default:"-" }}{{ run.entity_column|default:"-" }}{{ run.status }}
暂无已预处理的数据集。先去 上传页面 上传 CSV。
+ +
+ {% endif %} +
+{% endif %} + +{% if step == 2 %} +
+

Step 2: 配置聚类参数

+
+ + +
+ + + 按住 Ctrl 多选，留空则使用自动检测结果 +
+
+ + + 按住 Ctrl 多选，留空则自动选择前10个数值列。仅显示数值类型列。 +
+
+ + +
+
+ + +
+ ← 上一步 + +
+
+{% endif %} + +{% if step == 3 %} +
+

Step 3: 确认并运行

+

请确认以下配置，确认无误后点击"开始分析"：

+ + + + + + +
数据集 ID#{{ request.GET.run_id }}
实体列{{ request.GET|getlist:"entity_columns"|default:"自动检测" }}
特征列{{ request.GET.feature_columns|default:"自动选择" }}
聚类算法{{ request.GET.algorithm }}
最小簇大小{{ request.GET.min_cluster_size }}
+
+ ← 上一步 + +
+ +
+ +{% endif %} +{% endblock %} diff --git a/templates/tianxuan/upload.html b/templates/tianxuan/upload.html new file mode 100644 index 0000000..b01306c --- /dev/null +++ b/templates/tianxuan/upload.html @@ -0,0 +1,125 @@ +{% extends 'base.html' %} +{% block title %}上传数据 - 天璇{% endblock %} +{% block content %} + +
+

上传 CSV 文件

+

支持 .csv 文件，多文件批量上传，自动解压 .zip

+
+ {% csrf_token %} +
+
📂
+

拖拽 CSV 文件到此处，或点击选择

+ +
+ +
+
+ +
+ +
+

已上传的数据集

+ {% if runs %} + + + + {% for run in runs %} + + + + + + + + {% endfor %} + +
ID文件行数状态操作
#{{ run.id }}{{ run.csv_glob }}{{ run.total_flows|default:"-" }}{{ run.status }} + {% if run.status == 'loading' or run.status == 'profiling' or run.status == 'aggregating' or run.status == 'clustering' or run.status == 'extracting' %} + + {% else %} + + {% endif %} +
+ {% else %} +

暂无已上传的数据集

+ {% endif %} +
+ + +{% endblock %} diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_data_loader.py b/tests/test_data_loader.py new file mode 100644 index 0000000..a5398cf --- /dev/null +++ b/tests/test_data_loader.py @@ -0,0 +1,367 @@ +"""Tests for analysis.data_loader — BOM detection, schema validation, load/config.""" + +import csv +import os +import tempfile +from pathlib import Path + +import pytest +import polars as pl + +from analysis.data_loader import ( + _file_count, + _merge_schema_entries, + _resolve_encoding, + detect_bom, + load_config, + load_csv_directory, + validate_schemas, +) + + +# ═══════════════════════════════════════════════════════════════════════ +# BOM detection +# ═══════════════════════════════════════════════════════════════════════ + +class TestDetectBOM: + """detect_bom() should return the correct encoding string for each BOM.""" + + def test_utf8_bom(self): + """UTF-8 BOM (EF BB BF) → 'utf-8-sig'.""" + path = _write_bytes(b'\xef\xbb\xbfcol1,col2\n1,2\n') + try: + assert detect_bom(path) == 'utf-8-sig' + finally: + os.unlink(path) + + def test_utf16le_bom(self): + """UTF-16 LE BOM (FF FE) → 'utf-16le'.""" + path = _write_bytes(b'\xff\xfec\x00o\x00l\x001\x00,\x00c\x00o\x00l\x002\x00\n\x001\x00,\x002\x00\n') + try: + assert detect_bom(path) == 'utf-16le' + finally: + os.unlink(path) + + def test_utf16be_bom(self): + """UTF-16 BE BOM (FE FF) → 'utf-16be'.""" + path = _write_bytes(b'\xfe\xff\x00c\x00o\x00l\x001\x00,\x00c\x00o\x00l\x002\x00\n\x001\x00,\x002\x00\n') + try: + assert detect_bom(path) == 'utf-16be' + finally: + os.unlink(path) + + def test_no_bom(self): + """Plain ASCII/UTF-8 without BOM → 'utf-8'.""" + path = _write_bytes(b'col1,col2\n1,2\n') + try: + assert detect_bom(path) == 'utf-8' + finally: + os.unlink(path) + + def test_empty_file(self): + """Empty file → 'utf-8'.""" + path = _write_bytes(b'') + try: + assert detect_bom(path) == 'utf-8' + finally: + os.unlink(path) + + +# ═══════════════════════════════════════════════════════════════════════ +# Schema validation +# ═══════════════════════════════════════════════════════════════════════ + +class TestValidateSchemas: + """validate_schemas() must raise ValueError when column sets differ.""" + + def test_matching_schemas(self): + """Identical schemas → no exception.""" + schemas = [ + {':ips': 'Utf8', '8ack': 'Int64'}, + {':ips': 'Utf8', '8ack': 'Int64'}, + ] + validate_schemas(schemas) # should not raise + + def test_empty_list(self): + """Empty list → no exception.""" + validate_schemas([]) + + def test_schema_mismatch_missing_column(self): + """One file missing a column → ValueError.""" + schemas = [ + {':ips': 'Utf8', '8ack': 'Int64'}, + {':ips': 'Utf8'}, # missing '8ack' + ] + with pytest.raises(ValueError, match=r'missing columns'): + validate_schemas(schemas) + + def test_schema_mismatch_extra_column(self): + """One file has an extra column → ValueError.""" + schemas = [ + {':ips': 'Utf8'}, + {':ips': 'Utf8', 'extra_col': 'Int64'}, + ] + with pytest.raises(ValueError, match=r'extra columns'): + validate_schemas(schemas) + + def test_schema_mismatch_both_sides(self): + """Both missing and extra columns → ValueError with both messages.""" + schemas = [ + {'a': 'Int64', 'b': 'Utf8'}, + {'a': 'Int64', 'c': 'Float64'}, + ] + with pytest.raises(ValueError): + validate_schemas(schemas) + + +class TestMergeSchemaEntries: + """_merge_schema_entries() should merge without raising.""" + + def test_simple_merge(self): + merged, conflicts = _merge_schema_entries([ + {'a': 'Int64', 'b': 'Utf8'}, + {'a': 'Int64', 'c': 'Float64'}, + ]) + assert set(merged.keys()) == {'a', 'b', 'c'} + assert merged['a'] == 'Int64' + + def test_type_conflict(self): + """Different dtypes for same column → last wins, conflict recorded.""" + merged, conflicts = _merge_schema_entries([ + {'val': 'Int64'}, + {'val': 'Float64'}, + ]) + assert merged['val'] == 'Float64' + assert 'val' in conflicts + + +# ═══════════════════════════════════════════════════════════════════════ +# CSV loading +# ═══════════════════════════════════════════════════════════════════════ + +class TestLoadCSVDirectory: + """End-to-end load_csv_directory() tests with temporary CSV files.""" + + def test_basic_load(self): + """Single CSV → LazyFrame + correct schema + row/file counts.""" + with tempfile.TemporaryDirectory() as tmpdir: + path = Path(tmpdir) / 'test.csv' + _write_csv(path, [ + [':ips', ':ipd', '8ack'], + ['10.0.0.1', '10.0.0.2', '100'], + ['10.0.0.1', '10.0.0.3', '200'], + ['10.0.0.2', '10.0.0.4', '150'], + ]) + + lf, schema, row_count, file_count, memory_mb = load_csv_directory( + str(path), encoding='utf-8' + ) + + assert isinstance(lf, pl.LazyFrame) + assert ':ips' in schema + assert ':ipd' in schema + assert '8ack' in schema + assert file_count == 1 + assert row_count > 0 + assert memory_mb >= 0 + + # Verify data round-trip + df = lf.collect() + assert len(df) == 3 + assert set(df[':ips'].to_list()) == {'10.0.0.1', '10.0.0.2'} + + def test_multiple_files(self): + """Multiple CSVs with same schema → merged LazyFrame.""" + with tempfile.TemporaryDirectory() as tmpdir: + p1 = Path(tmpdir) / 'part1.csv' + p2 = Path(tmpdir) / 'part2.csv' + _write_csv(p1, [['x'], ['1'], ['2']]) + _write_csv(p2, [['x'], ['3'], ['4']]) + + glob_pattern = str(Path(tmpdir) / 'part*.csv') + lf, schema, row_count, file_count, memory_mb = load_csv_directory( + glob_pattern, encoding='utf-8' + ) + + assert file_count == 2 + df = lf.collect() + assert len(df) == 4 + assert sorted(df['x'].to_list()) == [1, 2, 3, 4] + + def test_no_files(self): + """No matching files → FileNotFoundError.""" + with pytest.raises(FileNotFoundError, match=r'No files match'): + load_csv_directory('__nonexistent_glarg_*.csv') + + def test_schema_mismatch_across_files_strict(self): + """Files with different column sets + schema_strict=True → ValueError.""" + with tempfile.TemporaryDirectory() as tmpdir: + _write_csv(Path(tmpdir) / 'a.csv', [['x', 'y'], ['1', '2']]) + _write_csv(Path(tmpdir) / 'b.csv', [['x'], ['3']]) + + with pytest.raises(ValueError, match=r'Schema mismatch'): + load_csv_directory(str(Path(tmpdir) / '*.csv'), schema_strict=True) + + def test_schema_mismatch_across_files_lenient(self): + """Files with different column sets + schema_strict=False → merge with nulls.""" + with tempfile.TemporaryDirectory() as tmpdir: + _write_csv(Path(tmpdir) / 'a.csv', [['x', 'y'], ['1', '2']]) + _write_csv(Path(tmpdir) / 'b.csv', [['x'], ['3']]) + + lf, schema, *_ = load_csv_directory(str(Path(tmpdir) / '*.csv')) + df = lf.collect() + assert len(df) == 2 + assert 'x' in df.columns + assert 'y' in df.columns + # Row from b.csv should have null for 'y' + assert df['y'].is_null().sum() == 1 + + def test_schema_override(self): + """schema_override forces a specific column dtype.""" + with tempfile.TemporaryDirectory() as tmpdir: + path = Path(tmpdir) / 'test.csv' + _write_csv(path, [['val'], ['123'], ['456']]) + + lf, schema, *_ = load_csv_directory( + str(path), encoding='utf-8', + schema_override={'val': 'Float64'}, + ) + + df = lf.collect() + assert df['val'].dtype == pl.Float64 + + def test_custom_delimiter(self): + """Tab-delimited file should parse correctly.""" + with tempfile.TemporaryDirectory() as tmpdir: + path = Path(tmpdir) / 'test.tsv' + with open(path, 'w', newline='') as f: + f.write('a\tb\n1\t2\n3\t4\n') + + lf, *_ = load_csv_directory(str(path), delimiter='\t') + df = lf.collect() + assert len(df) == 2 + assert df.columns == ['a', 'b'] + + def test_glob_with_subdirectory(self): + """Recursive glob should find files in subdirectories.""" + with tempfile.TemporaryDirectory() as tmpdir: + sub = Path(tmpdir) / 'sub' + sub.mkdir() + _write_csv(sub / 'nested.csv', [['x'], ['42']]) + + lf, schema, row_count, file_count, memory_mb = load_csv_directory( + str(Path(tmpdir) / '**' / '*.csv') + ) + assert file_count == 1 + assert row_count > 0 + + def test_latlon_with_plus_and_blank(self): + """CSV with lat/lon columns containing '+', '' loads without error + and the column is Float64 after cleaning.""" + with tempfile.TemporaryDirectory() as tmpdir: + path = Path(tmpdir) / 'latlon_test.csv' + with open(path, 'w', newline='') as f: + f.write('src_ip,latitude,longitude\n') + f.write('10.0.0.1,+,120.0\n') + f.write('10.0.0.2,35.5,\n') + f.write('10.0.0.3,-,\n') + f.write('10.0.0.4,40.0,121.5\n') + + lf, schema, *_ = load_csv_directory(str(path)) + df = lf.collect() + + # lat/lon columns should be Float64 in schema + assert 'latitude' in schema + assert 'longitude' in schema + assert 'latitude' in df.columns + assert 'longitude' in df.columns + + # Check dtypes are Float64 + assert df['latitude'].dtype == pl.Float64 + assert df['longitude'].dtype == pl.Float64 + + # Standalone '+' and blank should become null + assert df['latitude'].is_null().sum() == 2 # rows 0,2 + assert df['longitude'].is_null().sum() == 2 # rows 1,2 + + # Valid values survive + assert df['latitude'].drop_nulls().to_list() == [35.5, 40.0] + assert df['longitude'].drop_nulls().to_list() == [120.0, 121.5] + + +# ═══════════════════════════════════════════════════════════════════════ +# Config loading +# ═══════════════════════════════════════════════════════════════════════ + +class TestLoadConfig: + """load_config() YAML loading and error handling.""" + + def test_load_valid_yaml(self): + path = _write_yaml('key: value\nnested:\n inner: 42\n') + try: + config = load_config(path) + assert config['key'] == 'value' + assert config['nested']['inner'] == 42 + finally: + os.unlink(path) + + def test_load_empty_yaml(self): + path = _write_yaml('') + try: + config = load_config(path) + assert config == {} + finally: + os.unlink(path) + + def test_config_path_none(self): + assert load_config(None) == {} + + def test_config_not_found(self): + with pytest.raises(FileNotFoundError): + load_config('/__nonexistent__/config.yaml') + + +# ═══════════════════════════════════════════════════════════════════════ +# Utility functions +# ═══════════════════════════════════════════════════════════════════════ + +class TestResolveEncoding: + def test_bom_overrides_default(self): + assert _resolve_encoding('utf-8', 'utf-16le') == 'utf-16le' + + def test_no_bom_uses_default(self): + # Polars >= 1.0 uses 'utf8' (not 'utf-8') + assert _resolve_encoding('utf-8', 'utf-8') == 'utf8' + assert _resolve_encoding('latin-1', 'utf-8') == 'latin-1' + + +class TestFileCount: + def test_count(self): + assert _file_count(['a', 'b', 'c']) == 3 + assert _file_count([]) == 0 + + +# ═══════════════════════════════════════════════════════════════════════ +# Helpers +# ═══════════════════════════════════════════════════════════════════════ + +def _write_csv(path: Path, rows: list[list[str]]) -> None: + """Write rows as a CSV file.""" + with open(path, 'w', newline='') as f: + writer = csv.writer(f) + for row in rows: + writer.writerow(row) + + +def _write_bytes(data: bytes) -> str: + """Write raw bytes to a temp file; return the path string.""" + with tempfile.NamedTemporaryFile(suffix='.csv', delete=False) as f: + f.write(data) + return f.name + + +def _write_yaml(content: str) -> str: + """Write YAML string to a temp file; return the path string.""" + with tempfile.NamedTemporaryFile(suffix='.yaml', mode='w', delete=False) as f: + f.write(content) + return f.name diff --git a/tests/test_e2e.py b/tests/test_e2e.py new file mode 100644 index 0000000..b188c17 --- /dev/null +++ b/tests/test_e2e.py @@ -0,0 +1,365 @@ +"""End-to-end integration test for the full TLS analysis pipeline. + +Pipeline + 1. Create synthetic TLS flow CSV with known entity clusters. + 2. Load via ``load_csv_directory``. + 3. Store in ``SessionStore``. + 4. Auto-detect entity column (``detect_entity_column``). + 5. Aggregate flows → entity profiles (``aggregate_by_entity``). + 6. Run HDBSCAN clustering on aggregated numeric features. + 7. Extract per-cluster distinguishing features. + +All data is synthetic, deterministic (seed=42), and self-contained — +no external files or network access required. +""" + +import tempfile +from pathlib import Path + +import numpy as np +import polars as pl +import pytest + +from analysis.data_loader import load_csv_directory +from analysis.entity_detector import detect_entity_column +from analysis.entity_aggregator import aggregate_by_entity +from analysis.session_store import SessionStore + +# ═══════════════════════════════════════════════════════════════════════ +# Fixtures +# ═══════════════════════════════════════════════════════════════════════ + +@pytest.fixture(autouse=True) +def clean_store(): + """Reset the SessionStore singleton before and after each test.""" + store = SessionStore() + store.drop_all() + yield + store.drop_all() + + +# ═══════════════════════════════════════════════════════════════════════ +# Synthetic data generator +# ═══════════════════════════════════════════════════════════════════════ + +def create_synthetic_flows(seed: int = 42) -> pl.DataFrame: + """Generate deterministic TLS flow data with three known entity groups. + + Uses the user's exact column naming convention only. + + Returns + ------- + pl.DataFrame + ~1000 rows with columns. + Entities (``:ips``) are partitioned into three behavioural + groups that HDBSCAN should recover after aggregation. + """ + rng = np.random.default_rng(seed) + + # ── Entity profiles ────────────────────────────────────────────── + # Group A: 10 high-volume entities (50 flows each) + group_a = [ + { + ':ips': f'10.0.0.{i+1}', + 'n_flows': 50, + '8ack_mu': 50_000, '8ack_sigma': 5_000, + '4dur_mu': 30.0, '4dur_sigma': 5.0, + 'dst_range': (100, 150), + '0ver': 'TLSv1.3', + 'cipher_suite': 'TLS_AES_256_GCM_SHA384', + } + for i in range(10) + ] + + # Group B: 15 medium-volume entities (20 flows each) + group_b = [ + { + ':ips': f'10.0.0.{i+11}', + 'n_flows': 20, + '8ack_mu': 5_000, '8ack_sigma': 1_000, + '4dur_mu': 15.0, '4dur_sigma': 3.0, + 'dst_range': (150, 200), + '0ver': 'TLSv1.2', + 'cipher_suite': 'TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256', + } + for i in range(15) + ] + + # Group C: 25 low-volume entities (6 flows each) + group_c = [ + { + ':ips': f'10.0.0.{i+26}', + 'n_flows': 6, + '8ack_mu': 500, '8ack_sigma': 100, + '4dur_mu': 5.0, '4dur_sigma': 1.0, + 'dst_range': (200, 250), + '0ver': 'TLSv1.2', + 'cipher_suite': 'TLS_ECDHE_RSA_WITH_CHACHA20_POLY1305_SHA256', + } + for i in range(25) + ] + + entities = group_a + group_b + group_c + # Total flows: 10×50 + 15×20 + 25×6 = 500 + 300 + 150 = 950 + + # ── Generate flow rows ──────────────────────────────────────────── + rows: list[dict] = [] + dst_ports = [80, 443, 8080, 8443] + protos = ['TCP', 'UDP'] + proto_weights = [0.9, 0.1] + + for ent in entities: + lo, hi = ent['dst_range'] + for _ in range(ent['n_flows']): + dst_idx = int(rng.integers(lo, hi)) + row = { + ':ips': ent[':ips'], + ':ipd': f"10.0.{dst_idx >> 8}.{dst_idx & 0xff}", + ':prd': int(rng.choice(dst_ports)), + '8ack': int(max(0, rng.normal(ent['8ack_mu'], + ent['8ack_sigma']))), + '4dur': round(max(0.1, abs(rng.normal(ent['4dur_mu'], + ent['4dur_sigma']))), 2), + '1ipp': str(rng.choice(protos, p=proto_weights)), + '0ver': ent['0ver'], + 'cipher_suite': ent['cipher_suite'], + 'snam': f"host{rng.integers(1, 200)}.example.com", + 'timestamp': ( + f"2024-06-15 " + f"{rng.integers(0, 23):02d}:" + f"{rng.integers(0, 59):02d}:" + f"{rng.integers(0, 59):02d}" + ), + } + rows.append(row) + + return pl.DataFrame(rows) + + +# ═══════════════════════════════════════════════════════════════════════ +# Pipeline tests +# ═══════════════════════════════════════════════════════════════════════ + +class TestFullPipeline: + """End-to-end: synthetic CSV → load → entity detect → aggregate → cluster.""" + + def _write_csv(self, df: pl.DataFrame, tmpdir: str) -> str: + """Write a Polars DataFrame to a temp CSV; return the file path.""" + path = Path(tmpdir) / 'synthetic_flows.csv' + df.write_csv(path) + return str(path) + + def test_full_pipeline(self): + """Smoke-test the full pipeline — every step produces valid output.""" + with tempfile.TemporaryDirectory() as tmpdir: + # ── Step 1: Create synthetic data ───────────────────────────── + df_flows = create_synthetic_flows(seed=42) + assert len(df_flows) > 0, 'Synthetic data should not be empty' + + csv_path = self._write_csv(df_flows, tmpdir) + + # ── Step 2: Load data ───────────────────────────────────────── + lf, schema, row_count, file_count, memory_mb = load_csv_directory( + csv_path, encoding='utf-8', + ) + assert isinstance(lf, pl.LazyFrame), 'Expected LazyFrame' + assert file_count == 1 + assert row_count > 0 + assert memory_mb >= 0 + assert ':ips' in schema + + # ── Step 3: Store in session ────────────────────────────────── + store = SessionStore() + store.store_dataset( + 'e2e_ds', lf, schema=schema, + metadata={'row_count': row_count, 'csv_glob': csv_path}, + ) + + # ── Step 4: Entity detection ────────────────────────────────── + det_result = detect_entity_column('e2e_ds') + assert 'error' not in det_result, f'detect_entity_column failed: {det_result.get("error")}' + # :ips is the catch-all entity column (strings with good unique ratio) + assert det_result['recommended'] in (':ips', ':ipd', 'snam'), ( + f'Expected :ips, :ipd or snam, got {det_result["recommended"]}' + ) + assert len(det_result['candidates']) > 0 + + # Verify recommended_multi exists + assert 'recommended_multi' in det_result, 'recommended_multi field missing' + assert len(det_result['recommended_multi']) > 0, 'recommended_multi is empty' + + # ── Step 5: Entity aggregation ──────────────────────────────── + agg_lf, feature_cols = aggregate_by_entity(lf, ':ips', schema) + df_agg = agg_lf.collect() + + # Should be exactly 50 entities (10 + 15 + 25) + entity_count = len(df_agg) + assert entity_count == 50, f'Expected 50 entities, got {entity_count}' + + # Verify per-entity flow counts (sort by flow_count desc so groups + # are contiguous: A=50, B=20, C=6) + df_sorted = df_agg.sort('flow_count', descending=True) + # Group A: 10 entities × 50 flows + for i in range(10): + assert df_sorted['flow_count'][i] == 50, ( + f'Row {i}: expected 50, got {df_sorted["flow_count"][i]}' + ) + # Group B: 15 entities × 20 flows + for i in range(10, 25): + assert df_sorted['flow_count'][i] == 20, ( + f'Row {i}: expected 20, got {df_sorted["flow_count"][i]}' + ) + # Group C: 25 entities × 6 flows + for i in range(25, 50): + assert df_sorted['flow_count'][i] == 6, ( + f'Row {i}: expected 6, got {df_sorted["flow_count"][i]}' + ) + + # Core feature columns are present + for col in ('flow_count', 'total_bytes_sent', + 'avg_duration', 'unique_dst_ips', 'unique_dst_ports', + 'unique_tls_versions', 'unique_protocols', + 'first_seen', 'last_seen'): + assert col in feature_cols, f'Missing feature column: {col}' + + # ── Step 6: Clustering ──────────────────────────────────────── + numeric_cols = [ + 'flow_count', 'total_bytes_sent', + 'avg_duration', 'unique_dst_ips', 'unique_dst_ports', + ] + available_cols = [c for c in numeric_cols if c in df_agg.columns] + + data = df_agg.select(available_cols).to_numpy() + assert data.shape[0] == 50, 'All 50 entities should have features' + + # Standard-scale + HDBSCAN + from sklearn.preprocessing import StandardScaler + from sklearn.cluster import HDBSCAN + + data_scaled = StandardScaler().fit_transform(data) + clusterer = HDBSCAN(min_cluster_size=5, metric='euclidean') + labels = clusterer.fit_predict(data_scaled) + + n_clusters = len(set(labels) - {-1}) + assert n_clusters > 0, ( + f'HDBSCAN should find ≥1 cluster, found {n_clusters} ' + f'(labels={set(labels)})' + ) + + noise_count = int((labels == -1).sum()) + # At most 40% noise allowed (we have well-separated groups) + noise_ratio = noise_count / len(labels) + assert noise_ratio < 0.40, ( + f'Noise ratio too high: {noise_ratio:.2%} ' + f'({noise_count}/{len(labels)})' + ) + + # ── Step 7: Feature extraction ──────────────────────────────── + unique_labels = sorted(set(l for l in labels if l >= 0)) + assert len(unique_labels) > 0, 'Need at least one valid cluster' + + # Compute per-cluster distinguishing features using z-score + features = [] + for label in unique_labels: + mask = labels == label + cluster_data = data[mask] + for col_idx, col_name in enumerate(available_cols): + global_mean = float(data[:, col_idx].mean()) + global_std = float(data[:, col_idx].std()) or 1.0 + cluster_mean = float(cluster_data[:, col_idx].mean()) + z = (cluster_mean - global_mean) / global_std + features.append({ + 'cluster': int(label), + 'feature': col_name, + 'z_score': round(z, 4), + }) + + assert len(features) > 0, 'Expected at least one extracted feature' + # Each cluster should have features for all numeric columns + assert len(features) >= len(unique_labels) * len(available_cols) + + # ── Step 8: Subnet aggregation function ──────────────────────── + from analysis.entity_aggregator import ip_to_subnet + assert callable(ip_to_subnet) + assert ip_to_subnet('10.0.1.15', 24) == '10.0.1.0/24' + + # Note: SessionStore is cleaned up by the ``clean_store`` fixture + + def test_synthetic_data_shape(self): + """Verify the synthetic data generator produces expected shapes.""" + df = create_synthetic_flows(seed=42) + assert len(df.columns) == 10, f'Expected 10 columns, got {len(df.columns)}' + # Total: 10×50 + 15×20 + 25×6 = 950 + assert len(df) == 950, f'Expected 950 rows, got {len(df)}' + + # Verify unique entity count + n_entities = df[':ips'].n_unique() + assert n_entities == 50, f'Expected 50 unique :ips, got {n_entities}' + + # Group A should have TLSv1.3 + group_a_ips = [f'10.0.0.{i+1}' for i in range(10)] + group_a_tls = df.filter( + pl.col(':ips').is_in(group_a_ips) + )['0ver'].unique().to_list() + assert group_a_tls == ['TLSv1.3'] + + # Group C should have CHACHA20 cipher + group_c_ips = [f'10.0.0.{i+26}' for i in range(25)] + group_c_cipher = df.filter( + pl.col(':ips').is_in(group_c_ips) + )['cipher_suite'].unique().to_list() + assert any('CHACHA20' in c for c in group_c_cipher) + + +class TestPipelineEdgeCases: + """Edge cases for the analysis pipeline.""" + + def test_empty_dataset_rejected(self): + """Empty dataset stored → detect_entity_column returns error.""" + store = SessionStore() + empty = pl.DataFrame({':ips': pl.Series([], dtype=pl.Utf8)}) + schema = {} + store.store_dataset( + 'empty_ds', empty.lazy(), schema=schema, + metadata={'row_count': 0}, + ) + result = detect_entity_column('empty_ds') + # May fail or return empty candidates; either is acceptable + # but it should not crash with an unhandled exception. + assert 'error' in result or result['recommended'] == '' + + def test_single_entity_clustering(self): + """Single entity → aggregation works but clustering may not split.""" + data = pl.DataFrame({ + ':ips': ['10.0.0.1'] * 20, + ':ipd': ['10.0.0.2'] * 20, + '8ack': [100] * 20, + '4dur': [1.0] * 20, + '1ipp': ['TCP'] * 20, + '0ver': ['TLSv1.2'] * 20, + 'cipher_suite': ['AES'] * 20, + 'snam': ['host1.example.com'] * 20, + 'timestamp': ['2024-01-01 00:00:00'] * 20, + ':prd': [443] * 20, + }) + schema = {name: str(dtype) for name, dtype in zip(data.columns, data.dtypes)} + lf = data.lazy() + agg_lf, _ = aggregate_by_entity(lf, ':ips', schema) + df_agg = agg_lf.collect() + + assert len(df_agg) == 1 + assert df_agg['flow_count'][0] == 20 + + def test_aggregate_preserves_schema_types(self): + """Aggregated columns should have expected Polars dtypes.""" + data = pl.DataFrame({ + ':ips': ['10.0.0.1'] * 10, + '8ack': [100] * 10, + '4dur': [1.5] * 10, + }) + schema = {name: str(dtype) for name, dtype in zip(data.columns, data.dtypes)} + lf = data.lazy() + agg_lf, _ = aggregate_by_entity(lf, ':ips', schema) + df = agg_lf.collect() + + assert df['flow_count'].dtype == pl.UInt32 or df['flow_count'].dtype == pl.Int64 diff --git a/tests/test_entity.py b/tests/test_entity.py new file mode 100644 index 0000000..838f843 --- /dev/null +++ b/tests/test_entity.py @@ -0,0 +1,322 @@ +"""Tests for entity detection (entity_detector) and aggregation (entity_aggregator). + +Every test isolates the SessionStore singleton via the ``clean_store`` fixture. +""" + +import pytest +import polars as pl + +from analysis.session_store import SessionStore +from analysis.entity_detector import _score_name, detect_entity_column +from analysis.entity_aggregator import ( + _find_column, + aggregate_by_entity, + build_aggregation_params, + ip_to_subnet, +) + + +# ═══════════════════════════════════════════════════════════════════════ +# Fixtures +# ═══════════════════════════════════════════════════════════════════════ + +@pytest.fixture(autouse=True) +def clean_store(): + """Reset the SessionStore singleton before and after each test.""" + store = SessionStore() + store.drop_all() + yield + store.drop_all() + + +def _store_dataset( + df: pl.DataFrame, + dataset_id: str = 'test_ds', +) -> str: + """Helper: store a DataFrame as a dataset in the session store. + + Returns the dataset_id. + """ + store = SessionStore() + schema = {name: str(dtype) for name, dtype in zip(df.columns, df.dtypes)} + store.store_dataset( + dataset_id=dataset_id, + lazyframe=df.lazy(), + schema=schema, + metadata={'row_count': len(df)}, + ) + return dataset_id + + +# ═══════════════════════════════════════════════════════════════════════ +# _score_name (unit-level) +# ═══════════════════════════════════════════════════════════════════════ + +class TestScoreName: + """_score_name() scores column names against entity keywords.""" + + def test_exact_match(self): + """Exact keyword match → 3.0.""" + score, kw = _score_name(':ips') + assert score == 3.0 + assert kw == ':ips' + + def test_suffix_match(self): + """Suffix match via _ separator → 3.0.""" + score, kw = _score_name('my_snam') + assert score == 3.0 + assert kw == 'snam' + + def test_prefix_match(self): + """Prefix match via _ separator → 3.0.""" + score, kw = _score_name('snam_value') + assert score == 3.0 + assert kw == 'snam' + + def test_substring_not_matched(self): + """Substring-only match now returns 0.0 (exact matching only).""" + score, kw = _score_name('myipaddr') + assert score == 0.0 + assert kw == '' + + def test_no_match(self): + score, kw = _score_name('random_column_xyz') + assert score == 0.0 + assert kw == '' + + def test_hyphen_normalised(self): + """Hyphen normalised to underscore for matching.""" + score, kw = _score_name('cipher-suite') + assert score == 3.0 + assert kw == 'cipher_suite' + + +# ═══════════════════════════════════════════════════════════════════════ +# detect_entity_column (integration-level) +# ═══════════════════════════════════════════════════════════════════════ + +class TestDetectEntityColumn: + """detect_entity_column() should find entity columns from stored data.""" + + def test_detect_ips(self): + """Dataset with ``:ips`` → recommended is ``:ips``.""" + data = pl.DataFrame({ + ':ips': [f'10.0.0.{i}' for i in range(1, 51)] * 10, + ':ipd': [f'10.0.1.{i}' for i in range(1, 501)], + '8ack': [100] * 500, + }) + ds_id = _store_dataset(data) + result = detect_entity_column(ds_id) + + assert result['recommended'] == ':ips' + assert len(result['candidates']) > 0 + assert result['candidates'][0]['column'] == ':ips' + assert result['total_rows'] == 500 + assert result['scored_columns'] >= 1 + + def test_detect_snam(self): + """Datasets with ``snam`` but no IP column → snam is recommended.""" + data = pl.DataFrame({ + 'snam': [f'host{i}.example.com' for i in range(100)], + '4dur': [float(i) for i in range(100)], + }) + ds_id = _store_dataset(data) + result = detect_entity_column(ds_id) + + assert result['recommended'] == 'snam' + assert result['candidates'][0]['matched_keyword'] == 'snam' + + def test_no_entity_column(self): + """No entity keywords matched → candidates still found but matched_keyword empty.""" + data = pl.DataFrame({ + 'col_a': [str(i) for i in range(100)], + 'col_b': [str(i * 2) for i in range(100)], + 'value_f64': [float(i) for i in range(100)], + }) + ds_id = _store_dataset(data) + result = detect_entity_column(ds_id) + + # String columns are candidates (detector scores on uniqueness, not just keywords) + assert len(result['candidates']) >= 2 + assert all(c['matched_keyword'] == '' for c in result['candidates']) + # recommended will be the highest-scoring string column + assert result['recommended'] in ('col_a', 'col_b') + + def test_all_numeric_no_strings(self): + """No string/categorical columns → no candidates.""" + data = pl.DataFrame({ + 'value': list(range(100)), + 'flag': [0, 1] * 50, + }) + ds_id = _store_dataset(data) + result = detect_entity_column(ds_id) + + assert result['recommended'] == '' + assert len(result['candidates']) == 0 + + def test_dataset_not_found(self): + """Unknown dataset_id → error response.""" + result = detect_entity_column('nonexistent_ds') + assert 'error' in result + assert result['recommended'] == '' + + +# ═══════════════════════════════════════════════════════════════════════ +# _find_column (helper for aggregation) +# ═══════════════════════════════════════════════════════════════════════ + +class TestFindColumn: + """_find_column() matches keywords against schema column names.""" + + ACK_KEYWORDS = ('8ack',) + + def test_exact_match(self): + schema = {'8ack': 'Int64', 'other': 'Utf8'} + col = _find_column(schema, self.ACK_KEYWORDS) + assert col == '8ack' + + def test_no_match(self): + schema = {'something_else': 'Int64'} + col = _find_column(schema, self.ACK_KEYWORDS) + assert col is None + + +# ═══════════════════════════════════════════════════════════════════════ +# aggregate_by_entity +# ═══════════════════════════════════════════════════════════════════════ + +class TestAggregateByEntity: + """aggregate_by_entity() groups flows and computes per-entity features.""" + + def test_two_entities(self): + """50 flows across 2 entities → 2 aggregated rows with correct counts.""" + data = pl.DataFrame({ + ':ips': ['10.0.0.1'] * 20 + ['10.0.0.2'] * 30, + ':ipd': ['10.0.1.1'] * 10 + ['10.0.1.2'] * 10 + + ['10.0.1.3'] * 30, + ':prd': [80] * 20 + [443] * 30, + '8ack': [100] * 20 + [200] * 30, + '4dur': [1.0] * 20 + [2.0] * 30, + '0ver': ['TLSv1.2'] * 20 + ['TLSv1.3'] * 30, + 'cipher_suite': ['AES'] * 20 + ['CHACHA20'] * 30, + '1ipp': ['TCP'] * 50, + 'timestamp': ['2024-01-01 00:00:00'] * 50, + }) + schema = {name: str(dtype) for name, dtype in zip(data.columns, data.dtypes)} + lf = data.lazy() + + agg_lf, feature_cols = aggregate_by_entity(lf, ':ips', schema) + df = agg_lf.collect() + + # Two entities + assert len(df) == 2 + + # Flow counts + df_sorted = df.sort(':ips') + assert df_sorted['flow_count'][0] == 20 # 10.0.0.1 + assert df_sorted['flow_count'][1] == 30 # 10.0.0.2 + + # Bytes — each column is now perfectly aligned with entity groups + assert df_sorted['total_bytes_sent'][0] == 20 * 100 + assert df_sorted['total_bytes_sent'][1] == 30 * 200 + + # Core feature columns present + assert 'flow_count' in feature_cols + assert 'total_bytes_sent' in feature_cols + assert 'avg_duration' in feature_cols + assert 'unique_dst_ips' in feature_cols + assert 'unique_dst_ports' in feature_cols + assert 'unique_protocols' in feature_cols + assert 'first_seen' in feature_cols + assert 'last_seen' in feature_cols + + def test_single_entity(self): + """Single entity → single aggregated row.""" + data = pl.DataFrame({ + ':ips': ['10.0.0.1'] * 5, + '8ack': [100] * 5, + ':ipd': ['10.0.0.2'] * 5, + }) + schema = {name: str(dtype) for name, dtype in zip(data.columns, data.dtypes)} + lf = data.lazy() + agg_lf, _ = aggregate_by_entity(lf, ':ips', schema) + df = agg_lf.collect() + assert len(df) == 1 + assert df['flow_count'][0] == 5 + + def test_nonexistent_entity_column(self): + """Unknown entity column → ValueError.""" + data = pl.DataFrame({'a': [1, 2, 3]}) + schema = {'a': 'Int64'} + lf = data.lazy() + with pytest.raises(ValueError, match=r"Entity column\(s\) '\[\'xyz\'\]'"): + aggregate_by_entity(lf, 'xyz', schema) + + def test_schema_with_missing_columns(self): + """Aggregation still works when some optional columns are missing.""" + data = pl.DataFrame({ + ':ips': ['10.0.0.1'] * 3 + ['10.0.0.2'] * 3, + ':ipd': ['10.0.0.3'] * 6, + # No 8ack, 4dur, etc. + }) + schema = {name: str(dtype) for name, dtype in zip(data.columns, data.dtypes)} + lf = data.lazy() + agg_lf, feature_cols = aggregate_by_entity(lf, ':ips', schema) + + df = agg_lf.collect() + assert len(df) == 2 + assert df['flow_count'][0] == 3 + # Missing features should have None values + assert df['total_bytes_sent'][0] is None + assert 'flow_count' in feature_cols + assert 'unique_dst_ips' in feature_cols + + +# ═══════════════════════════════════════════════════════════════════════ +# build_aggregation_params +# ═══════════════════════════════════════════════════════════════════════ + +class TestBuildAggregationParams: + """build_aggregation_params() builds correct expressions per schema.""" + + def test_flow_count_always_present(self): + params = build_aggregation_params({'a': 'Int64'}) + names = [p[0] for p in params] + assert 'flow_count' in names + + def test_ack_detected(self): + params = build_aggregation_params({'8ack': 'Int64'}) + names = [p[0] for p in params] + assert 'total_bytes_sent' in names + + def test_ack_not_detected(self): + params = build_aggregation_params({'x': 'Int64'}) + param_map = dict(params) + assert param_map['total_bytes_sent'] is not None # still has a fallback expr + # The value should be a Polars literal (None) + names = [p[0] for p in params] + assert 'total_bytes_sent' in names + + +# ═══════════════════════════════════════════════════════════════════════ +# ip_to_subnet (IP subnet mask conversion) +# ═══════════════════════════════════════════════════════════════════════ + +class TestIpToSubnet: + """ip_to_subnet() converts IP strings to subnet prefixes.""" + + def test_ip_to_subnet_24(self): + """ip_to_subnet with /24 mask.""" + assert ip_to_subnet('10.0.1.15', 24) == '10.0.1.0/24' + assert ip_to_subnet('192.168.1.255', 24) == '192.168.1.0/24' + + def test_ip_to_subnet_28(self): + """ip_to_subnet with /28 mask.""" + assert ip_to_subnet('10.0.1.15', 28) == '10.0.1.0/28' + assert ip_to_subnet('10.0.1.250', 28) == '10.0.1.240/28' + + def test_ip_to_subnet_invalid(self): + """ip_to_subnet with invalid inputs.""" + assert ip_to_subnet(None, 24) is None + assert ip_to_subnet('', 24) == '' + assert ip_to_subnet('not_an_ip', 24) == 'not_an_ip' diff --git a/tests/test_type_classifier.py b/tests/test_type_classifier.py new file mode 100644 index 0000000..d9df48f --- /dev/null +++ b/tests/test_type_classifier.py @@ -0,0 +1,421 @@ +"""Tests for the data type classifier engine (type_classifier) and IP +clustering utilities (ip_clustering). + +Covers: INT, FLOAT, ENUM, HEX, URL, IPv4, BOOL_ENUM detection via both +name-based and value-based heuristics, config override, schema-level +classification, and ``/24`` subnet grouping. +""" +import pytest +import polars as pl + +from analysis.type_classifier import ( + DataType, + classify_column, + classify_schema, + HEX_PATTERN, + URL_PATTERN, + IPv4_PATTERN, + BOOL_ENUM_VALUES, + LAT_LON_NAMES, +) +from analysis.ip_clustering import ip_to_int, get_subnet, cluster_ips + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_column — INT +# ═══════════════════════════════════════════════════════════════════════ + +class TestClassifyInt: + def test_int_series(self): + s = pl.Series('bytes_sent', [100, 200, 300]) + assert classify_column('bytes_sent', s) == DataType.INT + + def test_unsigned_int(self): + s = pl.Series('packets', [0, 1, 255, 65535]) + assert classify_column('packets', s) == DataType.INT + + def test_small_int64(self): + s = pl.Series('count', [1, 2, 3], dtype=pl.Int64) + assert classify_column('count', s) == DataType.INT + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_column — FLOAT +# ═══════════════════════════════════════════════════════════════════════ + +class TestClassifyFloat: + def test_float_series(self): + s = pl.Series('duration', [1.5, 2.7, 0.3]) + assert classify_column('duration', s) == DataType.FLOAT + + def test_float_dtype(self): + s = pl.Series('score', [0.1, 0.2], dtype=pl.Float32) + assert classify_column('score', s) == DataType.FLOAT + + def test_float_with_ints(self): + """Values look like ints but dtype is float → still FLOAT.""" + s = pl.Series('ratio', [1.0, 2.0, 3.0]) + assert classify_column('ratio', s) == DataType.FLOAT + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_column — ENUM (< 20 unique values) +# ═══════════════════════════════════════════════════════════════════════ + +class TestClassifyEnum: + def test_few_unique_strings(self): + s = pl.Series('tls_version', ['TLSv1.2', 'TLSv1.3', 'TLSv1.2']) + assert classify_column('tls_version', s) == DataType.ENUM + + def test_exactly_19_unique(self): + vals = [str(i) for i in range(19)] + s = pl.Series('version_code', vals * 2) + assert classify_column('version_code', s) == DataType.ENUM + + def test_many_unique_strings(self): + vals = [f'str_{i}' for i in range(50)] + s = pl.Series('desc', vals) + assert classify_column('desc', s) == DataType.STRING + + def test_name_version_hint(self): + """Column name contains 'version' → name heuristic returns ENUM.""" + s = pl.Series('tls_ver', ['1.2', '1.3', '1.2']) + assert classify_column('tls_ver', s) == DataType.ENUM + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_column — HEX ("0e a6 3f" format) +# ═══════════════════════════════════════════════════════════════════════ + +class TestClassifyHex: + def test_single_hex_pair(self): + s = pl.Series('cipher', ['0e', 'a6', '3f']) + assert classify_column('cipher', s) == DataType.HEX + + def test_multiple_hex_pairs(self): + s = pl.Series('cipher_suite', ['0e a6 3f', 'ab cd ef 01']) + assert classify_column('cipher_suite', s) == DataType.HEX + + def test_name_cipher_hint(self): + """Column name containing 'cipher' triggers name heuristic.""" + s = pl.Series('tls_cipher', ['ab cd', 'ef 01']) + assert classify_column('tls_cipher', s) == DataType.HEX + + def test_name_suite_hint(self): + s = pl.Series('suite_id', ['00 01', '00 02']) + assert classify_column('suite_id', s) == DataType.HEX + + def test_invalid_hex_not_matched(self): + """Not all values are valid hex pairs → should not be HEX.""" + s = pl.Series('x', ['0e a6', 'zz yy']) + result = classify_column('x', s) + assert result != DataType.HEX + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_column — URL +# ═══════════════════════════════════════════════════════════════════════ + +class TestClassifyUrl: + def test_https(self): + s = pl.Series('dst_url', ['https://example.com', 'https://test.org']) + assert classify_column('dst_url', s) == DataType.URL + + def test_http(self): + s = pl.Series('url', ['http://example.com/path', 'http://test.org']) + assert classify_column('url', s) == DataType.URL + + def test_name_url_hint(self): + s = pl.Series('request_uri', ['https://api.example.com/v1']) + assert classify_column('request_uri', s) == DataType.URL + + def test_not_url(self): + """Plain domain names without http:// should not be URL.""" + s = pl.Series('domain', ['example.com', 'test.org']) + assert classify_column('domain', s) != DataType.URL + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_column — IPv4 +# ═══════════════════════════════════════════════════════════════════════ + +class TestClassifyIPv4: + def test_ip_addresses(self): + s = pl.Series('ip', ['192.168.1.1', '10.0.0.1', '172.16.0.1']) + assert classify_column('ip', s) == DataType.IPv4 + + def test_ip_in_name(self): + s = pl.Series('src_ip', ['10.0.0.1', '10.0.0.2']) + # Name-based heuristic triggers first + assert classify_column('src_ip', s) == DataType.IPv4 + + def test_addr_suffix(self): + s = pl.Series('destination_addr', ['1.2.3.4', '5.6.7.8']) + assert classify_column('destination_addr', s) == DataType.IPv4 + + def test_invalid_octet_range(self): + """Octet > 255 → should not match IPv4 via values.""" + s = pl.Series('x', ['192.168.1.256', '10.0.0.1']) + result = classify_column('x', s) + assert result != DataType.IPv4 # 256 invalid, may fall through + + def test_ip_plus_normal_strings(self): + """Mix of IPs and non-IPs → not IPv4.""" + s = pl.Series('x', ['192.168.1.1', 'hello']) + result = classify_column('x', s) + assert result != DataType.IPv4 + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_column — BOOL_ENUM (+, '', true/false, etc.) +# ═══════════════════════════════════════════════════════════════════════ + +class TestClassifyBoolEnum: + def test_plus_and_blank(self): + s = pl.Series('flag', ['+', '', '+', ' ']) + assert classify_column('flag', s) == DataType.BOOL_ENUM + + def test_true_false(self): + s = pl.Series('active', ['true', 'false', 'true']) + assert classify_column('active', s) == DataType.BOOL_ENUM + + def test_yes_no(self): + s = pl.Series('enabled', ['yes', 'no', 'yes']) + assert classify_column('enabled', s) == DataType.BOOL_ENUM + + def test_01_strings(self): + s = pl.Series('ok', ['0', '1', '0']) + assert classify_column('ok', s) == DataType.BOOL_ENUM + + def test_boolean_dtype(self): + """Native pl.Boolean dtype → BOOL_ENUM.""" + s = pl.Series('flag', [True, False, True]) + assert classify_column('flag', s) == DataType.BOOL_ENUM + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_column — LAT_LON (name-based) +# ═══════════════════════════════════════════════════════════════════════ + +class TestClassifyLatLon: + def test_lat_column(self): + s = pl.Series('lat', [35.0, 36.5]) + assert classify_column('lat', s) == DataType.LAT_LON + + def test_latitude_column(self): + s = pl.Series('latitude', [40.0, 41.0]) + assert classify_column('latitude', s) == DataType.LAT_LON + + def test_lon_column(self): + s = pl.Series('lon', [120.0, 121.5]) + assert classify_column('lon', s) == DataType.LAT_LON + + def test_longitude_column(self): + s = pl.Series('longitude', [122.0, 123.0]) + assert classify_column('longitude', s) == DataType.LAT_LON + + def test_src_latitude_column(self): + """Column name src_latitude → LAT_LON via prefix heuristic.""" + s = pl.Series('src_latitude', [35.0, 36.0, 37.0]) + assert classify_column('src_latitude', s) == DataType.LAT_LON + + def test_dst_longitude_column(self): + """Column name dst_longitude → LAT_LON via prefix heuristic.""" + s = pl.Series('dst_longitude', [120.0, 121.0, 122.0]) + assert classify_column('dst_longitude', s) == DataType.LAT_LON + + def test_value_based_latlon_strings(self): + """String column with mix of '+', '', and valid floats → LAT_LON.""" + s = pl.Series('coord', ['+', '', '12.345', '-45.678', '90.0']) + assert classify_column('coord', s) == DataType.LAT_LON + + def test_value_based_latlon_rejects_out_of_range(self): + """String column with values > 180 → not LAT_LON.""" + s = pl.Series('coord', ['200', '300', '400']) + result = classify_column('coord', s) + assert result != DataType.LAT_LON + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_column — Config override (highest priority) +# ═══════════════════════════════════════════════════════════════════════ + +class TestConfigOverride: + def test_config_overrides_value(self): + """config_type='int' forces INT even if values are float.""" + s = pl.Series('anything', [1.5, 2.5, 3.5]) + assert classify_column('anything', s, config_type='int') == DataType.INT + + def test_config_overrides_name(self): + """config_type='string' forces STRING even if name matches IP.""" + s = pl.Series('src_ip', ['192.168.1.1', '10.0.0.1']) + assert classify_column('src_ip', s, config_type='string') == DataType.STRING + + def test_config_type_case_insensitive(self): + s = pl.Series('x', [1, 2]) + assert classify_column('x', s, config_type='FLOAT') == DataType.FLOAT + + def test_config_type_trimmed(self): + s = pl.Series('x', ['a', 'b']) + assert classify_column('x', s, config_type=' enum ') == DataType.ENUM + + def test_unknown_config_type_falls_through(self): + """Unknown config_type string → ignored, falls to name/value.""" + s = pl.Series('bytes_sent', [1, 2, 3]) + result = classify_column('bytes_sent', s, config_type='unknown_type') + assert result == DataType.INT # value-based + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_schema (full schema classification) +# ═══════════════════════════════════════════════════════════════════════ + +class TestClassifySchema: + def test_classify_all_columns(self): + data = { + 'src_ip': ['10.0.0.1', '10.0.0.2', '10.0.0.3'], + 'dst_ip': ['10.0.1.1', '10.0.1.2', '10.0.1.3'], + 'bytes_sent': [100, 200, 300], + 'duration': [1.5, 2.5, 0.5], + 'tls_version': ['TLSv1.2', 'TLSv1.3', 'TLSv1.2'], + 'cipher': ['0e a6 3f', 'ab cd ef', '01 02'], + 'url': ['https://a.com', 'https://b.com', 'https://c.com'], + 'lat': [35.0, 36.0, 37.0], + 'lon': [120.0, 121.0, 122.0], + } + lf = pl.DataFrame(data).lazy() + type_map = classify_schema(lf) + + assert type_map['src_ip'] == DataType.IPv4 + assert type_map['dst_ip'] == DataType.IPv4 + assert type_map['bytes_sent'] == DataType.INT + assert type_map['duration'] == DataType.FLOAT + assert type_map['tls_version'] == DataType.ENUM + assert type_map['cipher'] == DataType.HEX + assert type_map['url'] == DataType.URL + assert type_map['lat'] == DataType.LAT_LON + assert type_map['lon'] == DataType.LAT_LON + + def test_classify_with_config_overrides(self): + data = {'src_ip': ['10.0.0.1', '10.0.0.2']} + lf = pl.DataFrame(data).lazy() + overrides = {'src_ip': 'string'} + type_map = classify_schema(lf, config_overrides=overrides) + assert type_map['src_ip'] == DataType.STRING + + +# ═══════════════════════════════════════════════════════════════════════ +# IP clustering utilities +# ═══════════════════════════════════════════════════════════════════════ + +class TestIpToInt: + def test_simple(self): + assert ip_to_int('192.168.1.1') == 3232235777 + + def test_localhost(self): + assert ip_to_int('127.0.0.1') == 2130706433 + + def test_invalid(self): + assert ip_to_int('not_an_ip') is None + + def test_empty_string(self): + assert ip_to_int('') is None + + def test_out_of_range(self): + assert ip_to_int('999.999.999.999') is None + + +class TestGetSubnet: + def test_24_mask(self): + assert get_subnet('192.168.1.5', 24) == '192.168.1.0/24' + + def test_16_mask(self): + assert get_subnet('10.1.2.3', 16) == '10.1.0.0/16' + + def test_8_mask(self): + assert get_subnet('100.200.1.1', 8) == '100.0.0.0/8' + + def test_invalid_ip(self): + assert get_subnet('not_an_ip', 24) is None + + def test_default_mask(self): + """Default mask is /24.""" + assert get_subnet('10.0.0.15') == '10.0.0.0/24' + + +class TestClusterIps: + def test_single_subnet(self): + ips = ['10.0.0.1', '10.0.0.2', '10.0.0.3'] + groups = cluster_ips(ips, mask=24) + assert len(groups) == 1 + assert '10.0.0.0/24' in groups + assert len(groups['10.0.0.0/24']) == 3 + + def test_multiple_subnets(self): + ips = ['10.0.0.1', '10.0.1.1', '192.168.1.1'] + groups = cluster_ips(ips, mask=24) + assert len(groups) == 3 + + def test_invalid_ips_grouped(self): + ips = ['10.0.0.1', 'bad_ip', 'also_invalid'] + groups = cluster_ips(ips, mask=24) + assert '10.0.0.0/24' in groups + assert '__invalid__' in groups + assert len(groups['__invalid__']) == 2 + + def test_empty_list(self): + groups = cluster_ips([], mask=24) + assert groups == {} + + def test_same_subnet_then_16(self): + """10.0.x.x IPs with /16 mask all go to one subnet.""" + ips = ['10.0.1.1', '10.0.2.2', '10.0.255.255'] + groups = cluster_ips(ips, mask=16) + assert len(groups) == 1 + assert '10.0.0.0/16' in groups + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_column — MAC address (should NOT be HEX) +# ═══════════════════════════════════════════════════════════════════════ + +class TestClassifyMac: + def test_mac_address_detection(self): + """MAC addresses should NOT be classified as HEX.""" + s = pl.Series('mac', ['aa:bb:cc:dd:ee:ff', '00:1a:2b:3c:4d:5e']) + result = classify_column('mac', s) + assert result != DataType.HEX, f'MAC should not be HEX, got {result}' + # MAC with unique addresses → STRING (high cardinality) + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_column — PORT values (INT range → ENUM) +# ═══════════════════════════════════════════════════════════════════════ + +class TestClassifyPort: + def test_port_values_detection(self): + """Integer values in 1-65535 range should be ENUM.""" + s = pl.Series('port', ['80', '443', '8080', '8443']) + assert classify_column('port', s) == DataType.ENUM + + +# ═══════════════════════════════════════════════════════════════════════ +# classify_column — Value-based IPv4 (name-agnostic) +# ═══════════════════════════════════════════════════════════════════════ + +class TestClassifyValueBased: + def test_value_based_ipv4(self): + """IP detection should work from values alone.""" + s = pl.Series('col', ['10.0.0.1', '192.168.1.1', '8.8.8.8']) + assert classify_column('col', s) == DataType.IPv4 + + def test_value_based_url(self): + """URL detection should work from values.""" + s = pl.Series('col', ['https://example.com', 'http://test.org/path']) + assert classify_column('col', s) == DataType.URL + + def test_value_first_over_name(self): + """Value pattern should take priority over name heuristic.""" + # Column named "version" with IP-like values should be IPv4, not ENUM + s = pl.Series('version', ['10.0.0.1', '192.168.1.1']) + assert classify_column('version', s) == DataType.IPv4 diff --git a/tests/test_value_normalizer.py b/tests/test_value_normalizer.py new file mode 100644 index 0000000..3025e13 --- /dev/null +++ b/tests/test_value_normalizer.py @@ -0,0 +1,537 @@ +"""Tests for analysis.value_normalizer — TLS value normalisation. + +Covers: TLS version (hex & display), cipher suite (hex & display), +named curve (hex & display), CNRS/ISRS, and the top-level +:func:`normalize_lf` integration. +""" + +import pytest +import polars as pl + +from analysis.value_normalizer import ( + TLS_VERSION_MAP, + CIPHER_SUITE_MAP, + CURVE_MAP, + normalize_lf, +) + + +# ═════════════════════════════════════════════════════════════════════════ +# TLS Version +# ═════════════════════════════════════════════════════════════════════════ + +class TestNormTlsVersion: + """_norm_tls_version — hex & display → canonical enum name.""" + + def test_hex_03_04_tls_1_3(self): + lf = pl.LazyFrame({'0ver': ['03 04']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'tls_1_3' + + def test_hex_03_03_tls_1_2(self): + lf = pl.LazyFrame({'0ver': ['03 03']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'tls_1_2' + + def test_hex_03_02_tls_1_1(self): + lf = pl.LazyFrame({'0ver': ['03 02']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'tls_1_1' + + def test_hex_03_01_tls_1_0(self): + lf = pl.LazyFrame({'0ver': ['03 01']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'tls_1_0' + + def test_hex_02_00_ssl_2_0(self): + lf = pl.LazyFrame({'0ver': ['02 00']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'ssl_2_0' + + def test_hex_03_00_ssl_3_0(self): + lf = pl.LazyFrame({'0ver': ['03 00']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'ssl_3_0' + + def test_display_tlsv1_3(self): + lf = pl.LazyFrame({'0ver': ['TLSv1.3']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'tls_1_3' + + def test_display_tlsv1_2(self): + lf = pl.LazyFrame({'0ver': ['tlsv1.2']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'tls_1_2' + + def test_display_tls_1_1(self): + lf = pl.LazyFrame({'0ver': ['TLS 1.1']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'tls_1_1' + + def test_display_bare_1_3(self): + lf = pl.LazyFrame({'0ver': ['1.3']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'tls_1_3' + + def test_display_bare_1_2(self): + lf = pl.LazyFrame({'0ver': ['1.2']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'tls_1_2' + + def test_display_tlsv1(self): + lf = pl.LazyFrame({'0ver': ['TLSv1']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'tls_1_0' + + def test_display_sslv2(self): + lf = pl.LazyFrame({'0ver': ['SSLv2']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'ssl_2_0' + + def test_display_sslv3(self): + lf = pl.LazyFrame({'0ver': ['sslv3']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'ssl_3_0' + + def test_display_bare_2(self): + lf = pl.LazyFrame({'0ver': ['2']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'ssl_2_0' + + def test_display_bare_3(self): + lf = pl.LazyFrame({'0ver': ['3']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'ssl_3_0' + + def test_case_insensitive(self): + lf = pl.LazyFrame({'0ver': ['tlsv1.3', 'TLSv1.3', 'TlsV1.3']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'].to_list() == ['tls_1_3', 'tls_1_3', 'tls_1_3'] + + def test_unknown_version(self): + lf = pl.LazyFrame({'0ver': ['ff ff']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'unknown_version' + + def test_unknown_display(self): + lf = pl.LazyFrame({'0ver': ['TLSv9.9']}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] == 'unknown_version' + + def test_null_remains_null(self): + lf = pl.LazyFrame({'0ver': [None]}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'][0] is None + + def test_mixed_values(self): + lf = pl.LazyFrame({'0ver': ['03 04', 'tlsv1.2', '1.3', 'ssl 2', None]}) + result = normalize_lf(lf, {'0ver': None}).collect() + assert result['0ver_norm'].to_list() == [ + 'tls_1_3', 'tls_1_2', 'tls_1_3', 'ssl_2_0', None, + ] + + +# ═════════════════════════════════════════════════════════════════════════ +# Cipher Suite — hex +# ═════════════════════════════════════════════════════════════════════════ + +class TestNormCipher: + """_norm_cipher — hex cipher suite → canonical name.""" + + def test_hex_13_01(self): + lf = pl.LazyFrame({'0cph': ['13 01']}) + result = normalize_lf(lf, {'0cph': None}).collect() + assert result['0cph_norm'][0] == 'tls_aes_128_gcm_sha256' + + def test_hex_13_02(self): + lf = pl.LazyFrame({'0cph': ['13 02']}) + result = normalize_lf(lf, {'0cph': None}).collect() + assert result['0cph_norm'][0] == 'tls_aes_256_gcm_sha384' + + def test_hex_13_03(self): + lf = pl.LazyFrame({'0cph': ['13 03']}) + result = normalize_lf(lf, {'0cph': None}).collect() + assert result['0cph_norm'][0] == 'tls_chacha20_poly1305_sha256' + + def test_hex_c0_2b(self): + lf = pl.LazyFrame({'0cph': ['c0 2b']}) + result = normalize_lf(lf, {'0cph': None}).collect() + assert result['0cph_norm'][0] == 'tls_ecdhe_ecdsa_aes_128_gcm_sha256' + + def test_hex_00_35(self): + lf = pl.LazyFrame({'0cph': ['00 35']}) + result = normalize_lf(lf, {'0cph': None}).collect() + assert result['0cph_norm'][0] == 'tls_rsa_aes_128_cbc_sha' + + def test_hex_00_2f(self): + """00 2f also maps to tls_rsa_aes_128_cbc_sha (same canonical).""" + lf = pl.LazyFrame({'0cph': ['00 2f']}) + result = normalize_lf(lf, {'0cph': None}).collect() + assert result['0cph_norm'][0] == 'tls_rsa_aes_128_cbc_sha' + + def test_unknown_hex(self): + lf = pl.LazyFrame({'0cph': ['ab cd']}) + result = normalize_lf(lf, {'0cph': None}).collect() + assert result['0cph_norm'][0] == 'unknown_cipher_0xab cd' + + def test_null_remains_null(self): + lf = pl.LazyFrame({'0cph': [None]}) + result = normalize_lf(lf, {'0cph': None}).collect() + assert result['0cph_norm'][0] is None + + def test_case_insensitive_hex(self): + lf = pl.LazyFrame({'0cph': ['C0 2B']}) + result = normalize_lf(lf, {'0cph': None}).collect() + assert result['0cph_norm'][0] == 'tls_ecdhe_ecdsa_aes_128_gcm_sha256' + + +# ═════════════════════════════════════════════════════════════════════════ +# Cipher Suite — display name +# ═════════════════════════════════════════════════════════════════════════ + +class TestNormCipherDisplay: + """_norm_cipher_display — IANA/display name → canonical name.""" + + def test_iana_full_name(self): + lf = pl.LazyFrame({'cipher-suite': ['TLS_AES_128_GCM_SHA256']}) + result = normalize_lf(lf, {'cipher-suite': None}).collect() + assert result['cipher_suite_norm'][0] == 'tls_aes_128_gcm_sha256' + + def test_iana_full_name_lowercase(self): + lf = pl.LazyFrame({'cipher-suite': ['tls_aes_256_gcm_sha384']}) + result = normalize_lf(lf, {'cipher-suite': None}).collect() + assert result['cipher_suite_norm'][0] == 'tls_aes_256_gcm_sha384' + + def test_display_aes128gcm(self): + lf = pl.LazyFrame({'cipher-suite': ['AES 128 GCM']}) + result = normalize_lf(lf, {'cipher-suite': None}).collect() + assert result['cipher_suite_norm'][0] == 'tls_aes_128_gcm_sha256' + + def test_display_aes256gcm(self): + lf = pl.LazyFrame({'cipher-suite': ['AES 256 GCM']}) + result = normalize_lf(lf, {'cipher-suite': None}).collect() + assert result['cipher_suite_norm'][0] == 'tls_aes_256_gcm_sha384' + + def test_display_chacha20(self): + lf = pl.LazyFrame({'cipher-suite': ['CHACHA20-POLY1305']}) + result = normalize_lf(lf, {'cipher-suite': None}).collect() + assert result['cipher_suite_norm'][0] == 'tls_chacha20_poly1305_sha256' + + def test_display_ecdhe_ecdsa_aes128gcm(self): + lf = pl.LazyFrame({'cipher-suite': ['ECDHE-ECDSA-AES128-GCM']}) + result = normalize_lf(lf, {'cipher-suite': None}).collect() + assert result['cipher_suite_norm'][0] == 'tls_ecdhe_ecdsa_aes_128_gcm_sha256' + + def test_display_ecdhe_rsa_aes256gcm(self): + lf = pl.LazyFrame({'cipher-suite': ['ECDHE-RSA-AES256-GCM']}) + result = normalize_lf(lf, {'cipher-suite': None}).collect() + assert result['cipher_suite_norm'][0] == 'tls_ecdhe_rsa_aes_256_gcm_sha384' + + def test_display_rsa_aes128gcm(self): + lf = pl.LazyFrame({'cipher-suite': ['RSA AES 128 GCM']}) + result = normalize_lf(lf, {'cipher-suite': None}).collect() + assert result['cipher_suite_norm'][0] == 'tls_rsa_aes_128_gcm_sha256' + + def test_display_hyphen_variants(self): + lf = pl.LazyFrame({'cipher-suite': ['ECDHE-RSA-AES128-GCM']}) + result = normalize_lf(lf, {'cipher-suite': None}).collect() + assert result['cipher_suite_norm'][0] == 'tls_ecdhe_rsa_aes_128_gcm_sha256' + + def test_unknown_display(self): + lf = pl.LazyFrame({'cipher-suite': ['CUSTOM_CIPHER_999']}) + result = normalize_lf(lf, {'cipher-suite': None}).collect() + assert result['cipher_suite_norm'][0] == 'customcipher999_unknown' + + def test_null_remains_null(self): + lf = pl.LazyFrame({'cipher-suite': [None]}) + result = normalize_lf(lf, {'cipher-suite': None}).collect() + assert result['cipher_suite_norm'][0] is None + + def test_mixed_display_values(self): + lf = pl.LazyFrame({ + 'cipher-suite': [ + 'TLS_AES_128_GCM_SHA256', + 'AES 256 GCM', + 'ECDHE-RSA-AES128-GCM', + 'bogus', + None, + ], + }) + result = normalize_lf(lf, {'cipher-suite': None}).collect() + expected = [ + 'tls_aes_128_gcm_sha256', + 'tls_aes_256_gcm_sha384', + 'tls_ecdhe_rsa_aes_128_gcm_sha256', + 'bogus_unknown', + None, + ] + assert result['cipher_suite_norm'].to_list() == expected + + +# ═════════════════════════════════════════════════════════════════════════ +# Named Curve — hex +# ═════════════════════════════════════════════════════════════════════════ + +class TestNormCurve: + """_norm_curve — hex curve → canonical name.""" + + def test_hex_00_1d_x25519(self): + lf = pl.LazyFrame({'0crv': ['00 1d']}) + result = normalize_lf(lf, {'0crv': None}).collect() + assert result['0crv_norm'][0] == 'x25519' + + def test_hex_00_17_secp256r1(self): + lf = pl.LazyFrame({'0crv': ['00 17']}) + result = normalize_lf(lf, {'0crv': None}).collect() + assert result['0crv_norm'][0] == 'secp256r1' + + def test_hex_00_18_secp384r1(self): + lf = pl.LazyFrame({'0crv': ['00 18']}) + result = normalize_lf(lf, {'0crv': None}).collect() + assert result['0crv_norm'][0] == 'secp384r1' + + def test_hex_00_19_secp521r1(self): + lf = pl.LazyFrame({'0crv': ['00 19']}) + result = normalize_lf(lf, {'0crv': None}).collect() + assert result['0crv_norm'][0] == 'secp521r1' + + def test_hex_00_1e_x448(self): + lf = pl.LazyFrame({'0crv': ['00 1e']}) + result = normalize_lf(lf, {'0crv': None}).collect() + assert result['0crv_norm'][0] == 'x448' + + def test_unknown_hex_curve(self): + lf = pl.LazyFrame({'0crv': ['ff ff']}) + result = normalize_lf(lf, {'0crv': None}).collect() + assert result['0crv_norm'][0] == 'unknown_curve_0xff ff' + + def test_null_remains_null(self): + lf = pl.LazyFrame({'0crv': [None]}) + result = normalize_lf(lf, {'0crv': None}).collect() + assert result['0crv_norm'][0] is None + + def test_case_insensitive_hex_curve(self): + lf = pl.LazyFrame({'0crv': ['00 1D']}) + result = normalize_lf(lf, {'0crv': None}).collect() + assert result['0crv_norm'][0] == 'x25519' + + +# ═════════════════════════════════════════════════════════════════════════ +# Named Curve — display name +# ═════════════════════════════════════════════════════════════════════════ + +class TestNormCurveDisplay: + """_norm_curve_display — display name → canonical name.""" + + def test_x25519(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['X25519']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'x25519' + + def test_curve25519(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['Curve25519']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'x25519' + + def test_secp256r1(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['secp256r1']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'secp256r1' + + def test_prime256v1(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['prime256v1']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'secp256r1' + + def test_p256(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['P256']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'secp256r1' + + def test_p_256(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['P-256']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'secp256r1' + + def test_p_256_underscore(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['P_256']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'secp256r1' + + def test_secp384r1(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['secp384r1']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'secp384r1' + + def test_p384(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['P384']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'secp384r1' + + def test_p_384(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['p-384']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'secp384r1' + + def test_secp521r1(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['Secp521r1']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'secp521r1' + + def test_p521(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['P521']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'secp521r1' + + def test_x448(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['x448']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'x448' + + def test_curve448(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['Curve448']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'x448' + + def test_unknown_curve_display(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['brainpoolP256r1']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] == 'unknown_curve_brainpoolp256r1' + + def test_null_remains_null(self): + lf = pl.LazyFrame({'ecdhe-named-curve': [None]}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'][0] is None + + def test_case_insensitive_p256(self): + lf = pl.LazyFrame({'ecdhe-named-curve': ['P-256', 'p_256', 'P256']}) + result = normalize_lf(lf, {'ecdhe-named-curve': None}).collect() + assert result['ecdhe_named_curve_norm'].to_list() == [ + 'secp256r1', 'secp256r1', 'secp256r1', + ] + + +# ═════════════════════════════════════════════════════════════════════════ +# CNRS / ISRS +# ═════════════════════════════════════════════════════════════════════════ + +class TestNormCnrs: + """_norm_cnrs — '+' → 'yes', blank/null → 'no'.""" + + def test_plus_yes(self): + lf = pl.LazyFrame({'cnrs': ['+']}) + result = normalize_lf(lf, {'cnrs': None}).collect() + assert result['cnrs_norm'][0] == 'yes' + + def test_blank_no(self): + lf = pl.LazyFrame({'cnrs': ['']}) + result = normalize_lf(lf, {'cnrs': None}).collect() + assert result['cnrs_norm'][0] == 'no' + + def test_null_no(self): + lf = pl.LazyFrame({'cnrs': [None]}) + result = normalize_lf(lf, {'cnrs': None}).collect() + assert result['cnrs_norm'][0] == 'no' + + def test_whitespace_plus(self): + lf = pl.LazyFrame({'cnrs': [' + ']}) + result = normalize_lf(lf, {'cnrs': None}).collect() + assert result['cnrs_norm'][0] == 'yes' + + def test_anything_else_no(self): + lf = pl.LazyFrame({'cnrs': ['-', 'true', 'maybe', '0', '1']}) + result = normalize_lf(lf, {'cnrs': None}).collect() + assert result['cnrs_norm'].to_list() == ['no', 'no', 'no', 'no', 'no'] + + def test_isrs_plus_yes(self): + lf = pl.LazyFrame({'isrs': ['+']}) + result = normalize_lf(lf, {'isrs': None}).collect() + assert result['isrs_norm'][0] == 'yes' + + def test_isrs_blank_no(self): + lf = pl.LazyFrame({'isrs': ['']}) + result = normalize_lf(lf, {'isrs': None}).collect() + assert result['isrs_norm'][0] == 'no' + + +# ═════════════════════════════════════════════════════════════════════════ +# Integration — normalize_lf +# ═════════════════════════════════════════════════════════════════════════ + +class TestNormalizeLf: + """Top-level normalize_lf — column routing & no-op cases.""" + + def test_all_columns_at_once(self): + lf = pl.LazyFrame({ + '0ver': ['03 04'], + '0cph': ['13 01'], + 'cipher-suite': ['TLS_AES_128_GCM_SHA256'], + '0crv': ['00 1d'], + 'ecdhe-named-curve': ['X25519'], + 'cnrs': ['+'], + 'isrs': [''], + }) + type_map = { + '0ver': None, + '0cph': None, + 'cipher-suite': None, + '0crv': None, + 'ecdhe-named-curve': None, + 'cnrs': None, + 'isrs': None, + } + result = normalize_lf(lf, type_map).collect() + + assert result['0ver_norm'][0] == 'tls_1_3' + assert result['0cph_norm'][0] == 'tls_aes_128_gcm_sha256' + assert result['cipher_suite_norm'][0] == 'tls_aes_128_gcm_sha256' + assert result['0crv_norm'][0] == 'x25519' + assert result['ecdhe_named_curve_norm'][0] == 'x25519' + assert result['cnrs_norm'][0] == 'yes' + assert result['isrs_norm'][0] == 'no' + + def test_no_matching_columns(self): + """Empty type_map → no _norm columns added.""" + lf = pl.LazyFrame({'0ver': ['03 04']}) + result = normalize_lf(lf, {}).collect() + assert '0ver_norm' not in result.columns + assert result['0ver'][0] == '03 04' + + def test_subset_of_columns(self): + """Only columns present in type_map get normalised.""" + lf = pl.LazyFrame({ + '0ver': ['03 04'], + '0cph': ['13 01'], + 'cnrs': ['+'], + }) + result = normalize_lf(lf, {'0ver': None, 'cnrs': None}).collect() + assert '0ver_norm' in result.columns + assert 'cnrs_norm' in result.columns + assert '0cph_norm' not in result.columns + + def test_preserves_original_columns(self): + """Original columns are kept unchanged.""" + lf = pl.LazyFrame({ + '0ver': ['03 04'], + 'src_ip': ['10.0.0.1'], + }) + result = normalize_lf(lf, {'0ver': None}).collect() + assert '0ver' in result.columns + assert 'src_ip' in result.columns + assert result['src_ip'][0] == '10.0.0.1' + + def test_duplicate_normalized_column_exists(self): + """If a _norm column already exists, Polars appends suffixed.""" + lf = pl.LazyFrame({ + '0ver': ['03 04'], + '0ver_norm': ['old'], + }) + result = normalize_lf(lf, {'0ver': None}).collect() + # Polars will create 0ver_norm with new value + assert result['0ver_norm'][0] == 'tls_1_3' + + def test_lazyness_preserved(self): + """normalize_lf returns a LazyFrame, not materialised.""" + lf = pl.LazyFrame({'0ver': ['03 04']}) + result = normalize_lf(lf, {'0ver': None}) + assert isinstance(result, pl.LazyFrame) diff --git a/tianxuan/__init__.py b/tianxuan/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tianxuan/llm_orchestrator.py b/tianxuan/llm_orchestrator.py new file mode 100644 index 0000000..5f8e628 --- /dev/null +++ b/tianxuan/llm_orchestrator.py @@ -0,0 +1,85 @@ +"""LLM orchestrator optimized for 32B model via MCP tool calling. +Short prompts, aggressive truncation, step-by-step guidance.""" +import json, logging, time, urllib.request, urllib.error, asyncio +from typing import Optional +from analysis.tool_registry import handle_call + +logger = logging.getLogger('tianxuan.llm') + +TOOLS = [ + {"type": "function", "function": {"name": "profile_data", + "description": "Examine dataset columns and statistics", + "parameters": {"type": "object", "properties": {"dataset_id": {"type": "string"}}, "required": ["dataset_id"]}}}, + {"type": "function", "function": {"name": "build_entity_profiles", + "description": "Group flows by entity for user behavior analysis", + "parameters": {"type": "object", "properties": {"dataset_id": {"type": "string"}, "auto_detect": {"type": "boolean"}}, "required": ["dataset_id"]}}}, + {"type": "function", "function": {"name": "run_clustering", + "description": "Cluster entities into behavior groups", + "parameters": {"type": "object", "properties": {"dataset_id": {"type": "string"}, "cluster_columns": {"type": "array", "items": {"type": "string"}}, "algorithm": {"type": "string", "enum": ["hdbscan", "kmeans"]}}, "required": ["dataset_id", "cluster_columns"]}}}, + {"type": "function", "function": {"name": "extract_features", + "description": "Identify distinguishing features per cluster", + "parameters": {"type": "object", "properties": {"dataset_id": {"type": "string"}, "cluster_result_id": {"type": "string"}, "method": {"type": "string", "enum": ["zscore", "anova"]}}, "required": ["dataset_id", "cluster_result_id"]}}}, +] + +class LLMConfig: + def __init__(self, base_url="", api_key="", model="deepseek-v4-flash", max_tokens=2048, temperature=0.1): + self.base_url = base_url.rstrip('/') + self.api_key = api_key + self.model = model + self.max_tokens = max_tokens + self.temperature = temperature + +def call_llm(messages, config): + payload = json.dumps({"model": config.model, "messages": messages, "tools": TOOLS, + "tool_choice": "auto", "max_tokens": config.max_tokens, "temperature": config.temperature}).encode() + headers = {"Content-Type": "application/json"} + if config.api_key: headers["Authorization"] = f"Bearer {config.api_key}" + req = urllib.request.Request(f"{config.base_url}/chat/completions", data=payload, headers=headers, method='POST') + logger.info(f'[LLM] model={config.model}') + start = time.monotonic() + with urllib.request.urlopen(req, timeout=90) as resp: + d = json.loads(resp.read().decode()) + logger.info(f'[LLM] latency_ms={int((time.monotonic()-start)*1000)}') + return d + +def run_llm_pipeline(dataset_id, entity_col="", config=None, callback=None): + if config is None: + from config import get_config; cfg = get_config() + config = LLMConfig(cfg.llm.base_url, cfg.llm.api_key, cfg.llm.model) + msgs = [{"role": "system", "content": "Analyze dataset step by step using the available tools."}, + {"role": "user", "content": f"Profile dataset {dataset_id}, then build entities, cluster, and extract features."}] + guides = ["", "Good. Now build entity profiles.", "Good. Now run clustering.", "Good. Now extract features.", "Good. Summarize results."] + gi = 0 + for step in range(15): + if step > 0 and gi < len(guides) and guides[gi]: + msgs.append({"role": "user", "content": guides[gi]}) + gi += 1 + if callback: + callback(step, "thinking", None) + resp = call_llm(msgs, config) + if not resp: + if callback: + callback(step, "error", "LLM call failed") + return {"error": "LLM call failed"} + msg = resp["choices"][0]["message"] + if "tool_calls" not in msg or not msg["tool_calls"]: + if callback: + callback(step, "complete", msg.get("content", "")) + return {"result": msg.get("content","") or "Complete", "steps": step+1} + msgs.append(msg) + for tc in msg["tool_calls"]: + f = tc.get("function",{}); name = f.get("name","") + try: args = json.loads(f.get("arguments","{}")) + except: args = {} + logger.info(f'[TOOL] {name}') + if callback: + callback(step, name, None) + try: result = asyncio.run(handle_call(name, args)) + except Exception as e: result = {"error": str(e)} + if callback: + callback(step, name, result) + msgs.append({"role": "tool", "tool_call_id": tc.get("id",""), + "content": json.dumps(result, default=str, ensure_ascii=False)[:1200]}) + if callback: + callback(15, "max_steps", None) + return {"error": "Max steps", "steps": 15} diff --git a/tianxuan/settings.py b/tianxuan/settings.py new file mode 100644 index 0000000..f7f9982 --- /dev/null +++ b/tianxuan/settings.py @@ -0,0 +1,113 @@ +import os +from pathlib import Path + +BASE_DIR = Path(__file__).resolve().parent.parent +os.makedirs(BASE_DIR / "logs", exist_ok=True) + +SECRET_KEY = "django-insecure-change-me-in-production" + +DEBUG = True + +import socket + +hostname = socket.gethostname() +try: + local_ip = socket.gethostbyname(hostname) +except: + local_ip = "127.0.0.1" +ALLOWED_HOSTS = ["localhost", "127.0.0.1", hostname, local_ip, "*"] + +INSTALLED_APPS = [ + "django.contrib.admin", + "django.contrib.auth", + "django.contrib.contenttypes", + "django.contrib.sessions", + "django.contrib.messages", + "django.contrib.staticfiles", + "analysis.apps.AnalysisConfig", +] + +MIDDLEWARE = [ + "django.middleware.security.SecurityMiddleware", + "django.contrib.sessions.middleware.SessionMiddleware", + "django.middleware.common.CommonMiddleware", + "django.middleware.csrf.CsrfViewMiddleware", + "django.contrib.auth.middleware.AuthenticationMiddleware", + "django.contrib.messages.middleware.MessageMiddleware", + "django.middleware.clickjacking.XFrameOptionsMiddleware", +] + +ROOT_URLCONF = "tianxuan.urls" + +TEMPLATES = [ + { + "BACKEND": "django.template.backends.django.DjangoTemplates", + "DIRS": [BASE_DIR / "templates"], + "APP_DIRS": True, + "OPTIONS": { + "context_processors": [ + "django.template.context_processors.debug", + "django.template.context_processors.request", + "django.contrib.auth.context_processors.auth", + "django.contrib.messages.context_processors.messages", + ], + }, + }, +] + +WSGI_APPLICATION = "tianxuan.wsgi.application" + +DATABASES = { + "default": { + "ENGINE": "django.db.backends.sqlite3", + "NAME": BASE_DIR / "db.sqlite3", + } +} + +# Set SQLite journal mode to DELETE to avoid WAL files during compression +from django.db.backends.signals import connection_created + + +def _set_sqlite_journal(sender, connection, **kwargs): + if connection.vendor == "sqlite": + with connection.cursor() as cursor: + cursor.execute("PRAGMA journal_mode=DELETE;") + + +connection_created.connect(_set_sqlite_journal) + +AUTH_PASSWORD_VALIDATORS = [] + +LANGUAGE_CODE = "zh-hans" +TIME_ZONE = "UTC" +USE_I18N = True +USE_TZ = True + +DATA_UPLOAD_MAX_NUMBER_FIELDS = 0 +DATA_UPLOAD_MAX_MEMORY_SIZE = 256 * 1024 * 1024 +FILE_UPLOAD_MAX_MEMORY_SIZE = 10 * 1024 * 1024 + +STATIC_URL = "static/" +STATICFILES_DIRS = [BASE_DIR / "static"] + +DEFAULT_AUTO_FIELD = "django.db.models.BigAutoField" + +LOGGING = { + "version": 1, + "disable_existing_loggers": False, + "formatters": { + "verbose": {"format": "[{asctime}] {levelname} {name}: {message}", "style": "{"} + }, + "handlers": { + "file": { + "class": "logging.handlers.RotatingFileHandler", + "filename": BASE_DIR / "logs" / "tianxuan.log", + "maxBytes": 10485760, + "backupCount": 5, + "formatter": "verbose", + "encoding": "utf-8", + }, + "stderr": {"class": "logging.StreamHandler", "formatter": "verbose"}, + }, + "root": {"handlers": ["file", "stderr"], "level": "INFO"}, +} diff --git a/tianxuan/urls.py b/tianxuan/urls.py new file mode 100644 index 0000000..298e650 --- /dev/null +++ b/tianxuan/urls.py @@ -0,0 +1,7 @@ +from django.contrib import admin +from django.urls import path, include + +urlpatterns = [ + path('admin/', admin.site.urls), + path('', include('analysis.urls')), +] diff --git a/tianxuan/wsgi.py b/tianxuan/wsgi.py new file mode 100644 index 0000000..4802e3b --- /dev/null +++ b/tianxuan/wsgi.py @@ -0,0 +1,18 @@ +import os, sqlite3 +from pathlib import Path +from django.core.wsgi import get_wsgi_application + +db_path = Path(__file__).resolve().parent.parent / 'db.sqlite3' +if db_path.exists(): + try: + conn = sqlite3.connect(str(db_path)) + conn.execute('PRAGMA integrity_check') + conn.close() + except Exception: + import shutil + backup = Path(str(db_path) + '.corrupted') + shutil.copy2(str(db_path), str(backup)) + db_path.unlink() + +os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'tianxuan.settings') +application = get_wsgi_application() diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..f3c0fc3 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1213 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" +resolution-markers = [ + "python_full_version >= '3.15' and sys_platform == 'win32'", + "python_full_version == '3.14.*' and sys_platform == 'win32'", + "python_full_version >= '3.15' and sys_platform != 'win32'", + "python_full_version == '3.14.*' and sys_platform != 'win32'", + "python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version < '3.14' and sys_platform != 'win32'", +] + +[[package]] +name = "annotated-types" 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