Improve vulnerability prediction performance using self-attention mechanism and convolutional neural network

Bingjie Duan; Xu Zhou; Xugang Wu

doi:10.1117/12.2639144

15 July 2022 Improve vulnerability prediction performance using self-attention mechanism and convolutional neural network

Bingjie Duan, Xu Zhou, Xugang Wu

Author Affiliations +

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 122580F (2022) https://doi.org/10.1117/12.2639144
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

With the vigorous development of the Internet, the number of commonly used software has also increased rapidly. The security and reliability of software have become important challenges that researchers must deal with. Fuzzing is a way of detecting vulnerabilities by providing unintended inputs to the target software and observing the final running results. During these years, fuzzing has proven its effectiveness in software security testing. But a large number of fuzzing tools rely solely on runtime information while testing software. Achieving static vulnerability prediction for programs in advance can greatly improve the efficiency of fuzzing. Vulnerability prediction aims to obtain the possibility of vulnerabilities in different parts of the program. The existing vulnerability prediction methods are relatively simple for feature extraction between basic blocks. We design a novel model combining self-attention mechanism and convolutional neural networks, which can extract and integrate the internal information of functions. Compared with the state-of-the-art V-Fuzz, our recall can be improved by 9.7 percentage points, and the accuracies of K-100~K-1000 can be higher than 90%.

Citation Download Citation

Bingjie Duan, Xu Zhou, and Xugang Wu "Improve vulnerability prediction performance using self-attention mechanism and convolutional neural network", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 122580F (15 July 2022); https://doi.org/10.1117/12.2639144

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KEYWORDS

Convolutional neural networks

Binary data

Neural networks

Feature extraction

Defense technologies

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