DEFI FRAUD DETECTION USING VERIFIABLE MACHINE LEARNING WITH ZK-SNARKS

Abstract

Decentralized Finance (DeFi) has revolutionized financial services by eliminating traditional intermediaries, but this openness creates new vulnerabilities that malicious actors exploit for fraud. The pseudonymous nature of blockchain transactions and lack of centralized oversight make traditional fraud detection methods inadequate for the DeFi ecosystem. This paper introduces ChainGuard, an end-to-end fraud detection system that leverages verifiable machine learning with zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs). ChainGuard utilizes a comprehensive approach that combines advanced feature extraction from Ethereum blockchain transaction data, optimized machine learning models, and on-chain verification through zk-SNARKs. Our solution enables privacy-preserving fraud detection while maintaining the ability to verify results without exposing sensitive transaction data and the internal architecture of the model. We demonstrate that ChainGuard achieves permissible accuracy in detecting fraudulent activities across Ethereum and various DeFi platforms while ensuring computational efficiency through multiple optimization techniques, including quantization. Experimental results show that our approach achieves performance comparable to traditional fraud detection methods while maintaining the decentralized and trustless nature of blockchain systems.