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train_risk_model.py

99 lines3,977 bytessha256 cb71b554884e
  1. """Train a linear risk-score model (0-100) on the LEASH merchant dataset.
  3. Model: logistic regression over engineered features (numeric + boolean
  4. flags + missingness indicators), trained on the label
  5. (confirmed_malicious vs likely_legitimate). The output probability is
  6. scaled to a 0-100 risk score.
  8. Usage:
  9. .venv/bin/python train_risk_model.py
  10. """
  12. import numpy as np
  13. import pandas as pd
  14. from sklearn.linear_model import LogisticRegression
  15. from sklearn.metrics import roc_auc_score
  16. from sklearn.model_selection import train_test_split
  18. CSV_PATH = "data/processed/dataset_features.csv"
  19. MODEL_OUT = "models/risk_score_linear.json"
  21. # Behavioral + threat features present in the data. Registry fields are
  22. # excluded as raw features (they only exist for companies -> the model
  23. # would just learn entity_type). Instead, HAS_REGISTRY is one feature.
  24. NUMERIC = ["company_age_days", "domain_age_days",
  25. "domain_typo_score", "brand_name_similarity"]
  26. BOOL = ["registry_found", "website_reachable", "https_enabled", "tls_valid",
  27. "dns_a_exists", "dns_mx_exists", "dns_txt_exists",
  28. "domain_privacy_proxy", "openphish_hit", "urlhaus_hit",
  29. "possible_brand_impersonation"]
  32. def engineer(df: pd.DataFrame) -> pd.DataFrame:
  33. """NaN-safe feature matrix: numerics imputed to median + indicator,
  34. booleans mapped True=1, False=0, missing=0.5 (unknown)."""
  35. X = pd.DataFrame(index=df.index)
  36. for c in NUMERIC:
  37. v = pd.to_numeric(df[c], errors="coerce")
  38. med = v.median()
  39. X[c] = v.fillna(med)
  40. X[f"{c}_missing"] = v.isna().astype(int)
  41. for c in BOOL:
  42. v = df[c]
  43. if v.dtype == object:
  44. v = v.map(lambda x: x if isinstance(x, (bool, np.bool_))
  45. else np.nan)
  46. X[c] = v.astype(float).fillna(0.5) # 0.5 = unknown
  47. # registry-based derived
  48. age = pd.to_numeric(df["company_age_days"], errors="coerce")
  49. X["young_registry"] = (age < 365).astype(float).where(age.notna(), 0.0)
  50. dage = pd.to_numeric(df["domain_age_days"], errors="coerce")
  51. X["young_domain"] = (dage < 365).astype(float).where(dage.notna(), 0.0)
  52. return X
  55. def main() -> None:
  56. df = pd.read_csv(CSV_PATH, low_memory=False)
  57. df = df[df["label"].isin(["confirmed_malicious",
  58. "likely_legitimate"])].copy()
  59. y = (df["label"] == "confirmed_malicious").astype(int)
  61. X = engineer(df)
  62. Xtr, Xte, ytr, yte = train_test_split(
  63. X, y, test_size=0.2, random_state=42, stratify=y)
  65. clf = LogisticRegression(max_iter=2000)
  66. clf.fit(Xtr, ytr)
  68. p = clf.predict_proba(Xte)[:, 1]
  69. print(f"Test ROC-AUC: {roc_auc_score(yte, p):.4f}")
  70. print(f"Risk score (0-100) on test set: "
  71. f"malicious median={100 * np.median(p[yte == 1]):.1f}, "
  72. f"legit median={100 * np.median(p[yte == 0]):.1f}")
  74. # ---- formula ---------------------------------------------------------
  75. # risk = 100 * sigmoid(b0 + sum(coef_i * x_i))
  76. coefs = pd.Series(clf.coef_[0], index=X.columns)
  77. intercept = float(clf.intercept_[0])
  78. print("\nFORMULA: risk = 100 / (1 + exp(-(b0 + sum(c_i * x_i))))")
  79. print(f"b0 (intercept) = {intercept:.4f}")
  80. print("\nCoefficients c_i:")
  81. print(coefs.round(4).sort_values(key=abs, ascending=False).to_string())
  83. # Save model artifact
  84. out = {"intercept": intercept,
  85. "coefficients": coefs.round(6).to_dict(),
  86. "features_numeric": NUMERIC, "features_bool": BOOL,
  87. "median_impute": {c: float(pd.to_numeric(df[c], errors="coerce")
  88. .median()) for c in NUMERIC},
  89. "notes": "risk = 100 * sigmoid(b0 + sum(c*x)); bools: 1=True, "
  90. "0=False, 0.5=unknown; numeric missing -> median "
  91. "+ missing indicator."}
  92. import json
  93. with open(MODEL_OUT, "w") as f:
  94. json.dump(out, f, indent=2)
  95. print(f"\nSaved -> {MODEL_OUT}")
  98. if __name__ == "__main__":
  99. main()