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processing/lookalike_detection.py

155 lines6,699 bytessha256 8fa5e8a166b8
  1. """Lookalike / impersonation risk features.
  3. Rules:
  4. - Similarity to a known brand is a RISK FEATURE, never a label.
  5. - A domain equal to a brand's own domain is not impersonation.
  6. - Homoglyph folding + punycode detection + suspicious brand-in-subdomain.
  7. """
  9. from __future__ import annotations
  11. try: # rapidfuzz if available, pure-python fallback otherwise
  12. from rapidfuzz import fuzz
  14. def _ratio(a: str, b: str) -> float:
  15. return fuzz.ratio(a, b) / 100.0
  17. def _partial(a: str, b: str) -> float:
  18. return fuzz.partial_ratio(a, b) / 100.0
  19. except ImportError: # fallback
  20. def _ratio(a: str, b: str) -> float:
  21. if not a or not b:
  22. return 0.0
  23. # cheap levenshtein
  24. prev = list(range(len(b) + 1))
  25. for i, ca in enumerate(a, 1):
  26. cur = [i]
  27. for j, cb in enumerate(b, 1):
  28. cur.append(min(prev[j] + 1, cur[j - 1] + 1, prev[j - 1] + (ca != cb)))
  29. prev = cur
  30. return 1.0 - prev[-1] / max(len(a), len(b))
  32. def _partial(a: str, b: str) -> float:
  33. shorter, longer = (a, b) if len(a) <= len(b) else (b, a)
  34. best = 0.0
  35. for i in range(0, max(1, len(longer) - len(shorter) + 1)):
  36. best = max(best, _ratio(shorter, longer[i:i + len(shorter)]))
  37. return best
  40. BRANDS: dict[str, str] = {
  41. # brand -> known primary domain (empty = name-only signal)
  42. "paypal": "paypal.com", "google": "google.com", "microsoft": "microsoft.com",
  43. "apple": "apple.com", "amazon": "amazon.com", "netflix": "netflix.com",
  44. "facebook": "facebook.com", "instagram": "instagram.com",
  45. "whatsapp": "whatsapp.com", "linkedin": "linkedin.com", "tiktok": "tiktok.com",
  46. "coinbase": "coinbase.com", "binance": "binance.com", "kraken": "kraken.com",
  47. "metamask": "metamask.io", "revolut": "revolut.com", "wise": "wise.com",
  48. "dhl": "dhl.com", "fedex": "fedex.com", "ups": "ups.com", "usps": "usps.com",
  49. "dpd": "dpd.com", "gls": "gls-group.com", "post": "post.ch",
  50. "twint": "twint.ch", "postfinance": "postfinance.ch", "ubs": "ubs.com",
  51. "migros": "migros.ch", "coop": "coop.ch", "digitec": "digitec.ch",
  52. "galaxus": "galaxus.ch", "brack": "brack.ch", "microspot": "microspot.ch",
  53. "interdiscount": "interdiscount.ch", "fust": "fust.ch", "sbb": "sbb.ch",
  54. "swisscom": "swisscom.ch", "sunrise": "sunrise.ch", "salt": "salt.ch",
  55. "zurich": "zurich.ch", "axa": "axa.ch", "helvetia": "helvetia.ch",
  56. "allianz": "allianz.ch", "raiffeisen": "raiffeisen.ch",
  57. "americanexpress": "americanexpress.com", "zalando": "zalando.ch", "aliexpress": "aliexpress.com",
  58. "temu": "temu.com", "shein": "shein.com", "wish": "wish.com",
  59. "ebay": "ebay.com", "etsy": "etsy.com", "booking": "booking.com",
  60. "airbnb": "airbnb.com", "skrill": "skrill.com", "neteller": "neteller.com",
  61. "westernunion": "westernunion.com", "moneygram": "moneygram.com",
  62. "chase": "chase.com", "wellsfargo": "wellsfargo.com", "hsbc": "hsbc.com",
  63. "barclays": "barclays.co.uk", "lloyds": "lloydsbank.com",
  64. "dbs": "dbs.com.sg", "ocbc": "ocbc.com", "uob": "uob.com.sg",
  65. }
  67. _HOMOGLYPH_MAP = str.maketrans({
  68. "0": "o", "1": "l", "3": "e", "4": "a", "5": "s", "7": "t", "8": "b",
  69. "6": "g", "@": "a", "$": "s", "!": "i", "|": "l", "©": "c",
  70. })
  73. def homoglyph_fold(s: str) -> str:
  74. return s.translate(_HOMOGLYPH_MAP)
  77. def _labels(domain: str) -> list[str]:
  78. return [p for p in domain.split(".") if p]
  81. def analyze_domain(domain: str | None) -> dict:
  82. """Return lookalike feature dict for a normalized domain (or None-domain)."""
  83. from processing.normalize_domain import normalize_domain, root_domain
  85. nd = normalize_domain(domain) or (domain or "").lower() or None
  86. out = {
  87. "possible_brand_impersonation": False,
  88. "closest_known_brand": None,
  89. "brand_name_similarity": None,
  90. "domain_typo_score": None,
  91. "homoglyph_detected": False,
  92. "punycode_domain": bool(nd and "xn--" in nd),
  93. "suspicious_subdomain_pattern": False,
  94. }
  95. if not nd:
  96. return out
  98. root = root_domain(nd) or nd
  99. root_labels = _labels(root)
  100. nd_labels = _labels(nd)
  101. # labels left of the registrable root = subdomain labels
  102. sub_len = max(len(nd_labels) - len(root_labels), 0)
  103. sub_labels = nd_labels[:sub_len]
  105. best_brand, best_sim, best_root, best_embedded = None, 0.0, None, False
  106. for brand, brand_domain in BRANDS.items():
  107. brand_root = brand_domain or f"{brand}.com"
  108. brand_token = brand.replace(" ", "")
  109. first_label = root_labels[0] if root_labels else root
  110. # headline similarity: root-vs-brand-domain, first label vs brand token
  111. sim = max(
  112. _ratio(root, brand_root),
  113. _ratio(first_label, brand_token),
  114. )
  115. # exact (homoglyph-folded) containment beats fuzzy partials: brand token
  116. # embedded in the domain (paypal-login.com, micros0ft-support.com)
  117. folded_token = homoglyph_fold(brand_token)
  118. folded_first = homoglyph_fold(first_label)
  119. folded_subs = homoglyph_fold(".".join(sub_labels)) if sub_labels else ""
  120. embedded = False
  121. if folded_token in folded_first:
  122. sim = 1.0
  123. embedded = True
  124. if folded_subs and folded_token in folded_subs:
  125. if root != brand_root:
  126. # brand embedded left of an unrelated registrable root
  127. out["suspicious_subdomain_pattern"] = True
  128. sim = 1.0
  129. embedded = True
  130. if sim > best_sim:
  131. best_brand, best_sim, best_root, best_embedded = brand, sim, brand_root, embedded
  133. # homoglyph: folding the root makes it clearly land on a brand domain
  134. folded_root = homoglyph_fold(root)
  135. if folded_root != root:
  136. for brand, brand_domain in BRANDS.items():
  137. brand_root = brand_domain or f"{brand}.com"
  138. if root != brand_root and _ratio(folded_root, brand_root) >= 0.9:
  139. out["homoglyph_detected"] = True
  140. break
  142. out["closest_known_brand"] = best_brand
  143. out["brand_name_similarity"] = round(min(best_sim, 1.0), 4)
  144. out["domain_typo_score"] = round(1.0 - min(best_sim, 1.0), 4)
  146. if best_brand and best_root and root == best_root:
  147. # the domain IS the brand's own domain — not impersonation
  148. out["possible_brand_impersonation"] = False
  149. elif best_brand and best_root:
  150. close = 0.80 <= best_sim < 0.995
  151. out["possible_brand_impersonation"] = bool(
  152. best_embedded or close or out["homoglyph_detected"]
  153. or out["suspicious_subdomain_pattern"] or out["punycode_domain"]
  154. )
  155. return out