Enhancing Blockchain Security Through Smart Contract Honeypot Detection Using Machine Learning

Abstract

This research describes how we implemented machine learning technology to help detect malicious participants on the Ethereum blockchain, including fake smart contracts and honeypots. Honeypots are built to fool attackers into approaching them, as they act like vulnerable smart contracts. The work unites data science, blockchain and machine learning to help the system tell apart honeypot contracts from other types. The chosen system collects all necessary information about Ethereum contracts from the Etherscan API and sorts the data by method, transaction behavior and flow of funds. These aspects are applied to create and review an XGBoost classifier model. The model is tested in three different ways: using real data as a reference, with attackers as adversarial honeypots and by looking at its cost-benefit analysis. This unique way of tracing enables greater ease of updating, fully automated analysis and stronger accuracy while watching smart contracts live. Using machine learning in honeypot detection is an important step toward protecting decentralized applications by spotting and handling threats early in the blockchain system. With XGBoost as its foundation, the suggested honeypot detection approach achieves a total accuracy of 98.78 %. The high proportion is a result of the model's impressive accuracy in identifying honeypot and non-honeypot electronic contracts.