A novel example of fuzzing testing based on deep neural network

Yayun Zhu; Baiji Hu; Baiyi Wang; Ziqing Lin; Jingyi Cao; Xiaojuan Zhang; Lin Jiang; Haixiang Wang

doi:10.1117/12.3014979

4 March 2024 A novel example of fuzzing testing based on deep neural network

Yayun Zhu, Baiji Hu, Baiyi Wang, Ziqing Lin, Jingyi Cao, Xiaojuan Zhang, Lin Jiang, Haixiang Wang

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 1298168 (2024) https://doi.org/10.1117/12.3014979
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

In recent years, the secure operation of power systems has grasped the focus of researchers' attention, and fuzzing test-based vulnerability mining has become a commonly used tool for evaluating its stability and robustness in the face of various anomalies. In fuzzing testing, test case generation is concerned with the ultimate performance of vulnerability mining, however, existing techniques generally focus only on coverage and not on the vulnerable parts. Therefore, based LSTM arrays that combined with residual connections, we propose a novel approach to learn the vulnerable parts of the power system to guide the process of fuzzing by utilizing the powerful feature representation capability of neural networks. Experiments show that the improved method can achieve faster convergence and higher vulnerability detection rate of neural networks, while the FNR of which is 20.4% and TPR is 79.6%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yayun Zhu, Baiji Hu, Baiyi Wang, Ziqing Lin, Jingyi Cao, Xiaojuan Zhang, Lin Jiang, and Haixiang Wang "A novel example of fuzzing testing based on deep neural network", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 1298168 (4 March 2024); https://doi.org/10.1117/12.3014979

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