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.

Key words: deep learning, vulnerability detection, fuzzing test, symbolic execution, differential evolution algorithm

摘要： 针对目前智能合约的混合模糊测试框架存在探索效率低下、测试用例生成不具有引导性、约束求解韧性差等问题，提出了一种改进版混合模糊检测框架DEST(differential evolution with solution time).该模型融合模糊测试与符号执行方法的优点对智能合约进行高效率的探测，融入差分进化(differential evolution, DE)算法优化测试用例的质量和全局搜索能力，通过长短期记忆神经网络模型(long shortterm memory, LSTM)框架学习可满足性模理论(satisfiability modulo theories, SMT)脚本特征预测求解时间，提升符号执行的求解效率.实验表明，DEST模型比最先进的基准模型漏洞检测率提高9.42%，平均代码覆盖率提高3.6%.

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