📚 Series Navigation

👉 [Part 1: AI, Blockchain, and Cloud: Who Actually Does What?](/posts/part-1-ai-blockchain-cloud-who-does-what/)

👉 [Part 2: Why Fully Decentralized AI Is (Mostly) a Myth](/posts/part-2-why-fully-decentralized-ai-is-a-myth/)

👉 [Part 3: Web3 Data -> Cloud ML Pipelines (Spark in Practice)](/posts/part-3-web3-data-to-cloud-ml-pipelines/)

👉 Part 4: AI for Blockchain Fraud & Anomaly Detection

👉 [Part 5: Smart Contracts + AI Agents: Autonomous Systems](/posts/part-5-smart-contracts-ai-agents-autonomous-systems/)

👉 [Part 6: Auditable AI: Using Blockchain for Trust & Governance](/posts/part-6-what-comes-next-predictions/)

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AI for Blockchain Fraud & Anomaly Detection

Fraud Is Behavioral

Most blockchain attacks do not break cryptography. They exploit human and system behavior. That means detection is about spotting deviations from normal activity, not finding a single magic signature.

Common Fraud Patterns

• Wash trading
• Sybil wallets
• Bot farms
• Flash-loan abuse

Feature Engineering Examples

These features are cheap to compute and hold up across chains.

Baseline anomaly detection (continuous scores; features defined)

import numpy as np
from sklearn.ensemble import IsolationForest

features = np.array([
    [10,  1.2, 0.5],
    [500, 80.0, 2.1],
    [20,  2.0, 0.7],
], dtype=float)

model = IsolationForest(contamination=0.01, random_state=42)
model.fit(features)

scores = model.decision_function(features)
risk_scores = -scores

Use the continuous scores to rank alerts before applying thresholds.

Blockchain Integration

• Store scores on-chain
• Trigger smart-contract rules
• Maintain immutable audit trail

On-chain writes should be sparse: store decisions or summaries, not every feature.

Conclusion

AI detects. Blockchain enforces.

📚 Further Reading

• Isolation Forest (Liu, Ting, Zhou, 2008) — PDF
• Rekt News (exploit writeups)
• De.Fi REKT Database (exploit index)
• Ronin bridge security breach postmortem
• Poly Network hack analysis (Elliptic)