peer reviewedBeyond the Public Mempool: Catching DeFi Attacks Before They Happen with Real-Time Smart Contract Analysis
The rise of decentralized finance has brought a vast range of opportunities to the blockchain space and many risks. This paper tackles the challenge of detecting malicious smart contracts on Ethereum designed to exploit vulnerabilities and cause financial losses. We present a novel approach for preemptively identifying malicious smart contracts during their deployment stage. For this purpose, we gathered a dataset comprising 161 malicious smart contracts and 5500 benign smart contracts. By introducing and extracting various features related to the deployer, transaction characteristics, and deployment bytecode and selecting the most impactful features, we developed multiple models using different machine learning (ML) classification algorithms, compared them using the set of most impactful features, and selected the most accurate one as our detection model. We compared the model's performance with a publicly available ML malicious smart contract detection tool to benchmark it. The results demonstrate that our model achieves a superior True Positive Rate while having a lower False Positive Rate. Our model achieved a 79.17% detection rate for malicious smart contracts while maintaining a False Positive rate of less than 1.8%. Our model provides swift detection capabilities by alerting users immediately after a contract's deployment, thus enabling timely response and risk mitigation.9. Industry, innovation and infrastructur
