Multi-Objective Approach for Detecting Vulnerabilities in Ethereum Smart Contracts

Abstract

Smart contracts, which provide user-defined logic to blockchains, have become increasingly popular in recent years due to their decentralized system architecture. They are executable programs that can automate transactions on the ethereum blockchain. However, security concerns with certain aspects of smart contracts can be challenging to address. Therefore, this paper proposed a multi-objective approach using a neural network makes it a more scalable and effective tool for detecting smart contract vulnerabilities than traditional approaches. With the increasing complexity of smart contracts and the growing importance of security in the blockchain space, a technique like multi-objective is becoming increasingly necessary to ensure the safety and reliability of decentralized applications. The proposed approach evaluated over 11000 real word ethereum smart contracts and detected two vulnerabilities without expert knowledge. The results showed that it achieved an average F1-score of 86.7 and 84.4 percent for reentrancy and timestamp vulnerability, respectively, indicating that the proposed approach has an impressive level of accuracy in classifying complex smart contracts, which has significant implications in the blockchain security field.