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processing/build_dataset.py

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  1. """Dataset build orchestrator.
  3. Usage (from repo root, venv active):
  4. python -m processing.build_dataset collect # downloads (cached/resumable)
  5. python -m processing.build_dataset enrich # bounded RDAP + DNS enrichment
  6. python -m processing.build_dataset build # parquet datasets + report
  8. dataset_raw.parquet one row per source record (pre-dedupe), raw_json kept
  9. dataset_clean.parquet entity-level, deduped, validated, labels applied
  10. dataset_features.parquet clean + RDAP/DNS/lookalike/age features
  11. """
  13. from __future__ import annotations
  15. import csv
  16. import datetime as dt
  17. import io
  18. import json
  19. import pathlib
  20. import re
  21. import sys
  23. from schemas.canonical_schema import ALL_COLUMNS, LABELS, build_feature_object, new_row
  24. from collectors import common, openphish, urlhaus, gleif as gleif_col, zefix
  25. from processing import entity_resolution, labeling
  26. from processing.feature_engineering import company_age_days, domain_age_days, parse_rdap_doc
  27. from processing.lookalike_detection import analyze_domain
  28. from processing.normalize_company import normalize_company_name
  29. from processing.normalize_domain import normalize_domain, root_domain
  31. ROOT = common.ROOT
  32. INTER = ROOT / "data" / "intermediate"
  33. PROCESSED = ROOT / "data" / "processed"
  34. SAMPLES = ROOT / "data" / "samples"
  37. def _write_jsonl(path: pathlib.Path, rows: list[dict]) -> None:
  38. path.parent.mkdir(parents=True, exist_ok=True)
  39. with path.open("w") as f:
  40. for r in rows:
  41. f.write(json.dumps(r, ensure_ascii=False, default=str) + "\n")
  44. def _read_jsonl(path: pathlib.Path) -> list[dict]:
  45. if not path.exists():
  46. return []
  47. return [json.loads(line) for line in path.read_text().splitlines() if line.strip()]
  50. # ---------------------------------------------------------------- collectors
  52. def step_collect() -> None:
  53. INTER.mkdir(parents=True, exist_ok=True)
  55. # --- OpenPhish
  56. urls, op_meta, _ = openphish.collect()
  57. op_rows = []
  58. for u in urls:
  59. d = normalize_domain(u)
  60. op_rows.append({
  61. "root_domain": root_domain(d), "domain": d, "url": u,
  62. "source": "openphish", "threat_type": "phishing",
  63. "first_seen": op_meta["retrieved_at"][:10], "last_seen": op_meta["retrieved_at"][:10],
  64. "url_source": "OpenPhish community feed",
  65. })
  66. _write_jsonl(INTER / "openphish_records.jsonl", op_rows)
  68. # --- URLhaus
  69. csv_path, uh_meta, _ = urlhaus.collect()
  70. uh_rows = []
  71. # the dump starts with '#' comment lines; the column header is one of them
  72. header = None
  73. body: list[str] = []
  74. with open(csv_path, encoding="utf-8") as f:
  75. for line in f:
  76. if line.startswith("#"):
  77. candidate = line.lstrip("# ").strip()
  78. if candidate.startswith("id,"):
  79. header = candidate.split(",")
  80. else:
  81. body.append(line)
  82. for row in csv.DictReader(io.StringIO("".join(body)), fieldnames=header):
  83. u = row.get("url") or ""
  84. d = normalize_domain(u)
  85. first = (row.get("date_added") or "")[:10] or uh_meta["retrieved_at"][:10]
  86. uh_rows.append({
  87. "root_domain": root_domain(d), "domain": d, "url": u,
  88. "source": "urlhaus", "threat_type": f"malware:{row.get('threat') or 'unknown'}",
  89. "first_seen": first, "last_seen": first,
  90. "url_source": row.get("urlhaus_link") or "https://urlhaus.abuse.ch/",
  91. "tags": row.get("tags"), "url_status": row.get("url_status"),
  92. })
  93. _write_jsonl(INTER / "urlhaus_records.jsonl", uh_rows)
  95. # --- GLEIF CH
  96. for country in common.CONFIG["gleif"]["countries"]:
  97. recs = gleif_col.collect(country)
  98. _write_jsonl(INTER / f"gleif_{country.lower()}_records.jsonl", recs)
  99. print(json.dumps({"source": "gleif", "country": country, "records": len(recs)}), file=sys.stderr)
  101. # --- Zefix: record access status (no data without token)
  102. probe = zefix.probe_unauthenticated()
  103. (common.raw_dir("zefix") / f"probe_{common.today()}.json").write_text(json.dumps(probe, indent=2))
  104. print(json.dumps({"source": "zefix", "status": probe}), file=sys.stderr)
  107. def step_enrich() -> None:
  108. """Bounded RDAP + DNS enrichment over known threat domains (cached/resumable)."""
  109. from collectors import rdap, dns_enrich
  111. op_rows = _read_jsonl(INTER / "openphish_records.jsonl")
  112. uh_rows = _read_jsonl(INTER / "urlhaus_records.jsonl")
  113. domains = []
  114. skipped_ipish = 0
  115. for r in op_rows + uh_rows:
  116. rd = r.get("root_domain")
  117. if not rd or rd in domains:
  118. continue
  119. # IP-literal hosts (e.g. "0.100" fragments of 0.100.14.129 from
  120. # IP-hosted URLhaus URLs) have no domain RDAP record; skip them.
  121. if not re.search(r"[a-z]", rd.lower()):
  122. skipped_ipish += 1
  123. continue
  124. domains.append(rd)
  126. print(json.dumps({"enrich": "rdap", "domains": min(len(domains), int(common.CONFIG["rdap"]["max_domains"])), "skipped_ipish": skipped_ipish}), file=sys.stderr)
  127. rdap.enrich(domains)
  128. dns_enrich.enrich(domains)
  129. print(json.dumps({"enrich": "done", "candidate_domains": len(domains)}), file=sys.stderr)
  132. # --------------------------------------------------------------- build steps
  134. def _gleif_attr(rec: dict) -> dict:
  135. a = rec.get("attributes") or {}
  136. ent = a.get("entity") or {}
  137. legal_addr = ent.get("legalAddress") or {}
  138. hq = ent.get("headquartersAddress") or {}
  139. # registration block lives at top level (attributes.registration); dates
  140. # fall back to entity.creationDate when the block is null
  141. reg = a.get("registration") or ent.get("registration") or {}
  142. legal_name = ent.get("legalName")
  143. if isinstance(legal_name, dict):
  144. legal_name = legal_name.get("name")
  145. lf = ent.get("legalForm") or {}
  146. if isinstance(lf, dict):
  147. legal_form = lf.get("name") or lf.get("id") or lf.get("other")
  148. else:
  149. legal_form = lf
  150. registered_as = ent.get("registeredAs")
  151. # Swiss UID (CHE-###.###.###) doubles as vat_uid when present
  152. vat_uid = registered_as if isinstance(registered_as, str) and registered_as.upper().startswith("CHE-") else None
  153. return {
  154. "lei": a.get("lei"),
  155. "legal_company_name": legal_name,
  156. "entity_status": ent.get("status"),
  157. "legal_form": legal_form,
  158. "incorporation_date": (reg.get("initialRegistrationDate") or ent.get("creationDate") or "")[:10] or None,
  159. "registry_last_updated": (reg.get("lastUpdateDate") or "")[:10] or None,
  160. "registered_address": " ".join(legal_addr.get("addressLines") or []) or None,
  161. "registered_country": legal_addr.get("country"),
  162. "registered_city": legal_addr.get("city"),
  163. "region": legal_addr.get("region"),
  164. "postal_code": legal_addr.get("postalCode"),
  165. "country": hq.get("country") or legal_addr.get("country"),
  166. "city": hq.get("city") or legal_addr.get("city"),
  167. "vat_uid": vat_uid,
  168. }
  171. def step_build() -> None:
  172. import pandas as pd
  174. PROCESSED.mkdir(parents=True, exist_ok=True)
  175. SAMPLES.mkdir(parents=True, exist_ok=True)
  176. now = common.utcnow()
  177. today = common.today()
  179. # ---------------- RAW: one row per source record
  180. raw_rows: list[dict] = []
  182. op_records = _read_jsonl(INTER / "openphish_records.jsonl")
  183. for r in op_records:
  184. raw_rows.append(new_row(
  185. entity_type="domain", domain=r["domain"], domain_normalized=r["domain"],
  186. openphish_hit=True, known_bad_domain=True,
  187. threat_sources="openphish", threat_types="phishing",
  188. threat_first_seen=r["first_seen"], threat_last_seen=r["last_seen"],
  189. threat_sample_urls=";".join([r["url"]]),
  190. label="confirmed_malicious", label_confidence=labeling.OPENPHISH_CONF,
  191. label_source="openphish",
  192. label_reason="URL present in OpenPhish confirmed phishing community feed.",
  193. sources="openphish",
  194. source_urls="https://openphish.com/feed.txt",
  195. collected_at=now, last_verified_at=now,
  196. collector_version=common.COLLECTOR_VERSION,
  197. data_license="OpenPhish community feed (non-commercial, attribution)",
  198. entity_key=r["root_domain"], raw_json=json.dumps(r, ensure_ascii=False),
  199. ))
  201. uh_records = _read_jsonl(INTER / "urlhaus_records.jsonl")
  202. for r in uh_records:
  203. raw_rows.append(new_row(
  204. entity_type="domain", domain=r["domain"], domain_normalized=r["domain"],
  205. urlhaus_hit=True, known_bad_domain=True,
  206. threat_sources="urlhaus",
  207. threat_types=r.get("threat_type") or "malware",
  208. threat_first_seen=r["first_seen"], threat_last_seen=r["last_seen"],
  209. threat_sample_urls=r["url"],
  210. label="confirmed_malicious", label_confidence=labeling.URLHAUS_CONF,
  211. label_source="urlhaus",
  212. label_reason="URL present in abuse.ch URLhaus recent malware feed.",
  213. sources="urlhaus",
  214. source_urls=r.get("url_source") or "https://urlhaus.abuse.ch/",
  215. collected_at=now, last_verified_at=now,
  216. collector_version=common.COLLECTOR_VERSION,
  217. data_license="abuse.ch URLhaus (free, attribution appreciated)",
  218. entity_key=r["root_domain"], raw_json=json.dumps(r, ensure_ascii=False),
  219. ))
  221. gleif_records = _read_jsonl(INTER / "gleif_ch_records.jsonl")
  222. for rec in gleif_records:
  223. a = _gleif_attr(rec)
  224. lbl, conf, lsrc, lreason = labeling.label_gleif(a["entity_status"])
  225. raw_rows.append(new_row(
  226. entity_type="company",
  227. merchant_name=a["legal_company_name"],
  228. normalized_merchant_name=normalize_company_name(a["legal_company_name"]),
  229. legal_company_name=a["legal_company_name"],
  230. country=a["country"], region=a["region"], city=a["city"],
  231. street_address=None, postal_code=a["postal_code"],
  232. registry_found=True, registry_lookup_status="ok",
  233. registry_source="gleif", registry_id=a["lei"], lei=a["lei"],
  234. vat_uid=a.get("vat_uid"),
  235. company_status=a["entity_status"], legal_form=a["legal_form"],
  236. incorporation_date=a["incorporation_date"],
  237. company_age_days=company_age_days(a["incorporation_date"]),
  238. registered_address=a["registered_address"],
  239. registered_country=a["registered_country"],
  240. registered_city=a["registered_city"],
  241. registry_last_updated=a["registry_last_updated"],
  242. label=lbl, label_confidence=conf, label_source=lsrc, label_reason=lreason,
  243. sources="gleif", source_urls="https://api.gleif.org/api/v1/lei-records",
  244. collected_at=now, last_verified_at=now,
  245. collector_version=common.COLLECTOR_VERSION,
  246. data_license="GLEIF LEI data (free and open, GLEIF terms of use)",
  247. entity_key=f"lei:{a['lei']}", raw_json=json.dumps(rec, ensure_ascii=False),
  248. ))
  250. raw_df = pd.DataFrame(raw_rows, columns=ALL_COLUMNS)
  251. raw_df.to_parquet(PROCESSED / "dataset_raw.parquet", index=False)
  253. # ---------------- CLEAN: entity-level dedupe + validation
  254. merged = entity_resolution.merge_threat_records(op_records + uh_records)
  255. threat_by_root = {m["entity_key"]: m for m in merged}
  257. clean_rows: list[dict] = []
  258. # threat entities (deduped by root domain across sources)
  259. for root, m in threat_by_root.items():
  260. srcs = set(m["sources"])
  261. lbl, conf, lsrc, lreason = labeling.label_threat(srcs)
  262. clean_rows.append(new_row(
  263. entity_type="domain", domain=m["root_domain"], domain_normalized=m["root_domain"],
  264. openphish_hit="openphish" in srcs, urlhaus_hit="urlhaus" in srcs,
  265. known_bad_domain=True,
  266. phishing_database_hits=1 if "openphish" in srcs else 0,
  267. malware_database_hits=1 if "urlhaus" in srcs else 0,
  268. threat_sources=";".join(m["sources"]),
  269. threat_types=";".join(m["threat_types"]),
  270. threat_first_seen=m["first_seen"], threat_last_seen=m["last_seen"],
  271. threat_sample_urls=";".join(m["urls"][:3]),
  272. label=lbl, label_confidence=conf, label_source=lsrc, label_reason=lreason,
  273. sources=";".join(m["sources"]),
  274. source_urls=";".join(dict.fromkeys(m["url_sources"])) or "https://openphish.com/feed.txt;https://urlhaus.abuse.ch/",
  275. collected_at=now, last_verified_at=now,
  276. collector_version=common.COLLECTOR_VERSION,
  277. data_license="OpenPhish community feed; abuse.ch URLhaus",
  278. entity_key=root, raw_json=json.dumps(m, ensure_ascii=False, default=str),
  279. ))
  280. # gleif entities (unique by LEI)
  281. seen_lei: set[str] = set()
  282. for rec in gleif_records:
  283. a = _gleif_attr(rec)
  284. if not a["lei"] or a["lei"] in seen_lei:
  285. continue
  286. seen_lei.add(a["lei"])
  287. lbl, conf, lsrc, lreason = labeling.label_gleif(a["entity_status"])
  288. clean_rows.append(new_row(
  289. entity_type="company",
  290. merchant_name=a["legal_company_name"],
  291. normalized_merchant_name=normalize_company_name(a["legal_company_name"]),
  292. legal_company_name=a["legal_company_name"],
  293. country=a["country"], region=a["region"], city=a["city"],
  294. postal_code=a["postal_code"],
  295. registry_found=True, registry_lookup_status="ok",
  296. registry_source="gleif", registry_id=a["lei"], lei=a["lei"],
  297. vat_uid=a.get("vat_uid"),
  298. company_status=a["entity_status"], legal_form=a["legal_form"],
  299. incorporation_date=a["incorporation_date"],
  300. company_age_days=company_age_days(a["incorporation_date"]),
  301. registered_address=a["registered_address"],
  302. registered_country=a["registered_country"],
  303. registered_city=a["registered_city"],
  304. registry_last_updated=a["registry_last_updated"],
  305. label=lbl, label_confidence=conf, label_source=lsrc, label_reason=lreason,
  306. sources="gleif", source_urls="https://api.gleif.org/api/v1/lei-records",
  307. collected_at=now, last_verified_at=now,
  308. collector_version=common.COLLECTOR_VERSION,
  309. data_license="GLEIF LEI data (free and open, GLEIF terms of use)",
  310. entity_key=f"lei:{a['lei']}", raw_json=None,
  311. ))
  313. clean_rows = entity_resolution.dedupe_by_entity_key(clean_rows)
  314. for r in clean_rows:
  315. r["merchant_id"] = entity_resolution.merchant_id_for(r["entity_key"])
  317. clean_df = pd.DataFrame(clean_rows, columns=ALL_COLUMNS)
  319. # validation
  320. problems: list[str] = []
  321. if clean_df["merchant_id"].duplicated().any():
  322. problems.append("duplicate merchant_id")
  323. bad_labels = set(clean_df["label"].dropna()) - set(LABELS)
  324. if bad_labels:
  325. problems.append(f"invalid labels: {sorted(bad_labels)}")
  326. missing_prov = clean_df["sources"].isna().sum() + clean_df["collected_at"].isna().sum()
  327. if missing_prov:
  328. problems.append(f"{missing_prov} rows missing provenance")
  329. if problems:
  330. print(json.dumps({"validation_problems": problems}), file=sys.stderr)
  332. clean_df = clean_df.convert_dtypes()
  333. clean_df.to_parquet(PROCESSED / "dataset_clean.parquet", index=False)
  335. # ---------------- FEATURES: RDAP + DNS + lookalike
  336. feat_df = clean_df.copy()
  338. rdap_docs = {}
  339. rdap_dir = common.raw_dir("rdap")
  340. for p in rdap_dir.glob("rdap_*.json"):
  341. try:
  342. rdap_docs[p.stem.removeprefix("rdap_")] = json.loads(p.read_text())
  343. except Exception:
  344. pass
  346. dns_docs = {}
  347. for p in (common.raw_dir("dns")).glob("dns_batch_*.json"):
  348. try:
  349. dns_docs.update(json.loads(p.read_text()))
  350. except Exception:
  351. pass
  353. # domain-typed rows get enrichment
  354. has_domain = feat_df["domain_normalized"].notna()
  355. as_of = dt.date.today()
  357. rdap_parsed = {}
  358. dns_parsed = {}
  359. for idx in feat_df.index[has_domain]:
  360. d = feat_df.at[idx, "domain_normalized"]
  361. doc = rdap_docs.get(d)
  362. rdap_parsed[idx] = parse_rdap_doc(doc) if doc else {"rdap_available": None}
  363. dd = dns_docs.get(d)
  364. dns_parsed[idx] = dd if dd else {}
  366. if rdap_parsed:
  367. for field in ["domain_creation_date", "domain_expiry_date", "registrar",
  368. "nameservers", "country_from_domain_registration"]:
  369. feat_df[field] = feat_df.index.map(lambda i: rdap_parsed.get(i, {}).get(field))
  370. feat_df["rdap_available"] = feat_df.index.map(
  371. lambda i: rdap_parsed.get(i, {}).get("rdap_available"))
  372. feat_df["domain_age_days"] = feat_df["domain_creation_date"].map(
  373. lambda c: domain_age_days(c, as_of))
  375. if dns_parsed:
  376. for field in ["dns_a_exists", "dns_mx_exists", "dns_txt_exists", "dns_error"]:
  377. feat_df[field] = feat_df.index.map(lambda i: dns_parsed.get(i, {}).get(field))
  379. # lookalike features for every row with a domain
  380. look = {}
  381. for idx in feat_df.index[has_domain]:
  382. look[idx] = analyze_domain(feat_df.at[idx, "domain_normalized"])
  383. for field in ["possible_brand_impersonation", "closest_known_brand", "brand_name_similarity",
  384. "domain_typo_score", "homoglyph_detected", "punycode_domain",
  385. "suspicious_subdomain_pattern"]:
  386. feat_df[field] = feat_df.index.map(lambda i: look.get(i, {}).get(field))
  387. # company rows: lookalike on normalized name (brand proximity as risk feature)
  388. has_name = feat_df["normalized_merchant_name"].notna() & ~has_domain
  389. for idx in feat_df.index[has_name]:
  390. name = feat_df.at[idx, "normalized_merchant_name"]
  391. from processing.lookalike_detection import _ratio, BRANDS
  392. best, bs = None, 0.0
  393. for brand in BRANDS:
  394. s = _ratio(name or "", brand)
  395. if s > bs:
  396. best, bs = brand, s
  397. feat_df.at[idx, "closest_known_brand"] = best
  398. feat_df.at[idx, "brand_name_similarity"] = round(min(bs, 1.0), 4)
  399. feat_df.at[idx, "domain_typo_score"] = round(1.0 - min(bs, 1.0), 4)
  400. feat_df.at[idx, "possible_brand_impersonation"] = bool(0.8 <= bs < 0.995)
  402. feat_df = feat_df.convert_dtypes()
  403. feat_df.to_parquet(PROCESSED / "dataset_features.parquet", index=False)
  405. # CSV exports (inspection convenience)
  406. feat_df.to_csv(PROCESSED / "dataset_features.csv", index=False)
  408. # sample feature object from a real GLEIF row
  409. sample = feat_df[feat_df["entity_type"] == "company"]
  410. if len(sample):
  411. obj = build_feature_object(sample.iloc[0].to_dict())
  412. (SAMPLES / "feature_object_example.json").write_text(json.dumps(obj, indent=2, ensure_ascii=False, default=str))
  414. print(json.dumps({
  415. "raw_rows": len(raw_df), "clean_rows": len(clean_df), "feature_rows": len(feat_df),
  416. "validation_problems": problems,
  417. }))
  420. if __name__ == "__main__":
  421. cmd = sys.argv[1] if len(sys.argv) > 1 else "build"
  422. if cmd == "collect":
  423. step_collect()
  424. elif cmd == "enrich":
  425. step_enrich()
  426. elif cmd == "build":
  427. step_build()
  428. elif cmd == "all":
  429. step_collect()
  430. step_enrich()
  431. step_build()
  432. else:
  433. raise SystemExit(f"unknown command: {cmd} (use collect|enrich|build|all)")