Automating Security in Blockchain: ML-Driven Smart Contract Vulnerability Analysis

Abstract

As with the increasing usage of blockchain especially in decentralized systems, the need for solid security measures increases to stop vulnerabilities in smart contracts. Our method is an ML-based solution to automate the detection of smart contract vulnerabilities and improves the blockchain security. In this paper, we propose a comprehensive vulnerability detection algorithm that employs a hybrid detection mechanism that analyzes transaction patterns and uses ML models to detect suspicious activities on the fly. The algorithm detects as well as flags smart contract vulnerabilities and malicious transactions by analyzing transaction amounts, user behavior, and frequency. The developed framework not only lessens manual effort but also automates validation so as to improve mitigation process in case of unforeseen risks. This essentially shows the capability of using ML-driven analysis to secure the blockchain environment as an adaptable approach for the detection of smart contract vulnerabilities.