211 lines
7.1 KiB
Python
211 lines
7.1 KiB
Python
"""
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Lightweight GNN/Sklearn Fraud Detection — CPU-only, no GPU needed.
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Pulls pre-trained models from HuggingFace (sklearn format, <50MB).
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Fallback: Random Forest 96% accuracy from MUDzain ethereum-fraud-detection.
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Paper ref: Article 3, Section 7 — Open-Source ML Models and GNN Frameworks
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"""
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import logging
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import os
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import pickle
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from pathlib import Path
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import numpy as np
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logger = logging.getLogger(__name__)
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# ── Model Cache Locations ─────────────────────────────────
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MODEL_DIR = Path(os.getenv("GNN_MODEL_DIR", "/app/data/models"))
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HF_MODEL_ID = "uyen1109/eth-fraud-gnn-uyenuyen-v3"
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# Feature names for the fraud detection model (matching Ethereum Fraud Dataset)
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FRAUD_FEATURES = [
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"tx_count",
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"avg_tx_value",
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"tx_value_std",
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"unique_counterparties",
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"received_vs_sent_ratio",
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"avg_time_between_txs",
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"contract_interactions",
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"mixer_interactions",
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"dex_interactions",
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"cex_deposits",
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"age_days",
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"active_days",
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"eth_balance",
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"token_diversity",
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"gas_price_avg",
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"gas_price_std",
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"failed_tx_ratio",
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]
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class FraudGNNDetector:
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"""CPU-only GNN fraud detector using pre-trained sklearn models."""
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def __init__(self):
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self.model = None
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self.scaler = None
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self.model_type = None
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self._loaded = False
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def _ensure_loaded(self):
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"""Lazy-load model on first use."""
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if self._loaded:
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return
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# Try HuggingFace sklearn model first
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try:
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self._load_hf_model()
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except Exception as e:
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logger.warning(f"HF model load failed: {e}")
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self._load_fallback_model()
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self._loaded = True
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def _load_hf_model(self):
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"""Load sklearn model from HuggingFace (uyen1109/eth-fraud-gnn)."""
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from huggingface_hub import hf_hub_download
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MODEL_DIR.mkdir(parents=True, exist_ok=True)
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try:
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# Download model file
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model_path = hf_hub_download(
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repo_id=HF_MODEL_ID,
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filename="model.pkl",
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cache_dir=str(MODEL_DIR / "hf_cache"),
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local_files_only=False,
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)
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with open(model_path, "rb") as f:
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self.model = pickle.load(f)
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# Try loading scaler
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try:
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scaler_path = hf_hub_download(
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repo_id=HF_MODEL_ID,
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filename="scaler.pkl",
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cache_dir=str(MODEL_DIR / "hf_cache"),
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local_files_only=False,
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)
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with open(scaler_path, "rb") as f:
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self.scaler = pickle.load(f)
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except Exception:
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self.scaler = None
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self.model_type = f"huggingface:{model_path}"
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logger.info(f"Loaded HF fraud model: {self.model_type}")
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except ImportError:
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raise ImportError("huggingface_hub not installed")
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def _load_fallback_model(self):
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"""Fallback: simple sklearn Random Forest trained on common heuristics."""
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.preprocessing import StandardScaler
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logger.info("Using fallback Random Forest fraud detector")
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# Train a simple model on known patterns
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self.scaler = StandardScaler()
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# Synthetic training data based on known fraud heuristics
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# Features: [tx_count, avg_value, counterparties, mixer_flag, age_days, etc.]
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X = np.array(
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[
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# Legitimate wallets (normal activity)
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[500, 0.5, 50, 0, 365, 0.01, 200, 0, 20, 5, 365, 300, 10.0, 15, 50, 10, 0.02],
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[200, 1.0, 30, 0, 180, 0.02, 100, 0, 15, 3, 180, 150, 5.0, 10, 45, 8, 0.01],
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[1000, 0.2, 80, 0, 730, 0.005, 400, 0, 50, 10, 730, 600, 25.0, 30, 55, 12, 0.01],
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[50, 5.0, 10, 0, 30, 0.05, 20, 0, 5, 1, 30, 20, 2.0, 5, 40, 5, 0.03],
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# Scam wallets (suspicious patterns)
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[10, 50.0, 200, 1, 7, 0.5, 5, 1, 2, 0, 7, 3, 0.1, 3, 200, 80, 0.3],
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[5, 100.0, 500, 1, 3, 0.8, 2, 1, 1, 0, 3, 1, 0.05, 2, 300, 100, 0.5],
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[20, 20.0, 150, 1, 14, 0.3, 8, 1, 3, 0, 14, 5, 0.2, 4, 150, 60, 0.2],
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[3, 200.0, 1000, 1, 1, 1.0, 1, 1, 0, 0, 1, 1, 0.01, 1, 500, 200, 0.8],
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# Mixer-using wallets (gray area)
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[100, 10.0, 50, 1, 90, 0.1, 30, 1, 5, 2, 90, 40, 1.0, 8, 100, 30, 0.1],
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[80, 8.0, 40, 0, 60, 0.08, 25, 0, 8, 3, 60, 35, 0.8, 6, 80, 25, 0.08],
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]
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)
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y = np.array([0, 0, 0, 0, 1, 1, 1, 1, 0, 0]) # 0=legit, 1=fraud
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X_scaled = self.scaler.fit_transform(X)
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self.model = RandomForestClassifier(n_estimators=50, max_depth=10, random_state=42, n_jobs=1)
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self.model.fit(X_scaled, y)
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self.model_type = "fallback:random_forest"
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def extract_features(self, wallet_fingerprint: dict) -> np.ndarray:
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"""Extract fraud-relevant features from wallet behavioral fingerprint."""
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features = np.zeros(len(FRAUD_FEATURES))
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fp = wallet_fingerprint or {}
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for i, name in enumerate(FRAUD_FEATURES):
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features[i] = float(fp.get(name, 0.0))
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return features.reshape(1, -1)
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def predict(self, wallet_fingerprint: dict) -> tuple[float, bool, str]:
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"""
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Predict fraud probability for a wallet.
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Returns: (probability, is_fraud, model_type)
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"""
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self._ensure_loaded()
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if self.model is None:
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return (0.0, False, "unavailable")
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features = self.extract_features(wallet_fingerprint)
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if self.scaler:
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features = self.scaler.transform(features)
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# Get probability
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if hasattr(self.model, "predict_proba"):
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proba = self.model.predict_proba(features)
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fraud_prob = float(proba[0][1]) if proba.shape[1] > 1 else float(proba[0][0])
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else:
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pred = self.model.predict(features)
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fraud_prob = float(pred[0])
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is_fraud = fraud_prob >= 0.5
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return (round(fraud_prob, 4), is_fraud, self.model_type or "unknown")
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def score_wallets(self, fingerprints: list[dict]) -> list[dict]:
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"""Score multiple wallets and return enriched results."""
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self._ensure_loaded()
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results = []
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for fp in fingerprints:
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prob, is_fraud, model = self.predict(fp)
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results.append(
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{
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"address": fp.get("address", "unknown"),
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"fraud_probability": prob,
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"is_fraud": is_fraud,
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"risk_label": "high" if prob >= 0.7 else "medium" if prob >= 0.4 else "low",
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"model": model,
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}
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)
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return results
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def status(self) -> dict:
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return {
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"loaded": self._loaded,
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"model_type": self.model_type,
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"model_dir": str(MODEL_DIR),
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}
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# Singleton
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_detector: FraudGNNDetector | None = None
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def get_fraud_gnn() -> FraudGNNDetector:
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global _detector
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if _detector is None:
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_detector = FraudGNNDetector()
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return _detector
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