blockchain_transaction_anomaly_detector
Overview
This model is a Tabular Transformer designed to identify anomalous patterns in blockchain transaction data. It analyzes features such as gas price, transaction frequency, contract interaction depth, and wallet age to flag potential security threats, money laundering, or smart contract exploits in real-time.
Model Architecture
The model implements a Temporal Tabular Transformer architecture.
- Feature Embedding: Continuous and categorical blockchain features are projected into a 128-dimensional latent space.
- Attention Mechanism: Multi-head attention layers allow the model to weigh the importance of different transaction attributes relative to the wallet's historical behavior.
- Inference: Binary classification output providing a probability score for anomaly detection.
Intended Use
- Wallet Security: Protecting users by flagging suspicious outbound transactions.
- AML Compliance: Assisting exchanges in identifying high-risk transaction flows.
- DeFi Protocol Monitoring: Detecting front-running attacks or flash-loan exploits on smart contracts.
Limitations
- Privacy Coins: Incapable of analyzing obfuscated transactions (e.g., Monero, Zcash).
- Zero-Day Exploits: May not recognize entirely new exploit patterns that differ fundamentally from the training set of known attacks.
- False Positives: High-frequency legitimate trading bots may occasionally be flagged as anomalous.
