Abstract
Due to the unique global state and transaction sequence characteristics of smart contracts, the detection method based on a single test case cannot improve the vulnerability detection rate during contract detection. The current contract testing methods based on genetic algorithms have not yet solved the problems caused by these characteristics. Therefore, we propose an adaptive fuzzing method based on dynamic taint analysis and genetic algorithm, SDTGfuzzer. SDTGfuzzer focuses on dynamic taint analysis to collect runtime information as feedback, and focuses on solving the challenges brought by global variables and transaction sequences for contract testing. Genetic Algorithms work well in test case generation for fuzzing. Therefore, SDTGfuzzer optimizes the genetic algorithm based on an efficient and lightweight multi-objective adaptive strategy, focusing on solving the problem that the contract constraints cannot be covered due to the global state. Experimental results show that our method has a higher vulnerability detection rate than other tools for detecting contract vulnerabilities.
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Acknowledgement
Natural Science Foundation of Shandong Province (Grant No. ZR2020ZD01).
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Zhao, H., Li, X., Gai, K. (2023). A Dynamic Taint Analysis-Based Smart Contract Testing Approach. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_38
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DOI: https://doi.org/10.1007/978-3-031-28124-2_38
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