# Part 4: AI for Blockchain Fraud & Anomaly Detection


## 📚 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

![Part 4 overview](/images/ai-web3-series/part4-fraud.jpg)

## 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

| Feature | Signal |
|---|---|
| tx_rate | Automation |
| counterparty_entropy | Wallet diversity |
| value_variance | Manipulation |

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

## Baseline anomaly detection (continuous scores; features defined)

```python
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](https://www.lamda.nju.edu.cn/publication/icdm08b.pdf)  
- [Rekt News (exploit writeups)](https://www.rekt.news/)  
- [De.Fi REKT Database (exploit index)](https://de.fi/rekt-database)  
- [Ronin bridge security breach postmortem](https://roninchain.com/blog/posts/back-to-building-ronin-security-breach-6513cc78a5edc1001b03c364)  
- [Poly Network hack analysis (Elliptic)](https://www.elliptic.co/blog/the-poly-network-hack-600-million-in-crypto-stolen-and-returned-in-24-hours)  


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> Author: Tom Wang  
> URL: https://blog-old.508.dev/posts/part-4-ai-for-blockchain-fraud-anomaly-detection/