A performance evaluation of deep-learnt features for software vulnerability detection

Ban, Xinbo, Liu, Shigang, Chen, Chao, and Chua, Caslon (2019) A performance evaluation of deep-learnt features for software vulnerability detection. Concurrency and Computation: Practice and Experience, 31 (19). e5103.

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Abstract

Software vulnerability is a critical issue in the realm of cyber security. In terms of techniques, machine learning (ML) has been successfully used in many real‐world problems such as software vulnerability detection, malware detection and function recognition, for high‐quality feature representation learning. In this paper, we propose a performance evaluation study on ML based solutions for software vulnerability detection, conducting three experiments: machine learning‐based techniques for software vulnerability detection based on the scenario of single type of vulnerability and multiple types of vulnerabilities per dataset; machine learning‐based techniques for cross‐project software vulnerability detection; and software vulnerability detection when facing the class imbalance problem with varying imbalance ratios. Experimental results show that it is possible to employ software vulnerability detection based on ML techniques. However, ML‐based techniques suffer poor performance on both cross‐project and class imbalance problem in software vulnerability detection.

Item ID: 64426
Item Type: Article (Research - C1)
ISSN: 1532-0634
Keywords: deep learning, security, software vulnerability
Copyright Information: © 2018 John Wiley & Sons.
Funders: National Natural Science Foundation of China
Projects and Grants: 61772405
Date Deposited: 22 Sep 2020 21:26
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0803 Computer Software > 080303 Computer System Security @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8903 Information Services > 890399 Information Services not elsewhere classified @ 100%
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