信息安全研究2026，Vol.12Issue(1)：24-32,9.DOI:10.12379/j.issn.2096-1057.2026.01.03
基于差分进化算法与求解时间预测的智能合约漏洞检测
Smart Contract Vulnerabilities Based on Differential Evolutionary Algorithms and Solution Time Prediction Detection
摘要
Abstract
Aiming at the problems of inefficient exploration,non-guided test case generation,and poor constraint-solving tenacity in current hybrid fuzzy testing frameworks for smart contracts,this paper proposes an improved hybrid fuzzy detection framework DEST.The model integrates the advantages of fuzzy testing and symbolic execution methods to efficiently detect smart contracts,incorporates the differential evolution(DE)algorithm to optimize the quality of test cases and global search capability,and learns SMT script features through LSTM framework to predict the solving time.The DEST model uses the differential evolutionary(DE)algorithm to optimize the quality of test cases and global search capability,and learns SMT script features through LSTM framework to predict the solving time,thereby improving the solving efficiency of symbolic execution.Experiments show that the DEST model improves vulnerability detection by 9.42％and average code coverage by 3.6％over the state-of-the-art benchmark model.关键词
深度学习/漏洞检测/模糊测试/符号执行/差分进化算法Key words
deep learning/vulnerability detection/fuzzing test/symbolic execution/differential evolution algorithm分类
信息技术与安全科学引用本文复制引用
Cai Lizhi,Ma Yuan,Yang Kang..基于差分进化算法与求解时间预测的智能合约漏洞检测[J].信息安全研究,2026,12(1):24-32,9.基金项目
上海市青年科技英才扬帆专项(24YF2720000) （24YF2720000）
