A performance evaluation of deep‐learnt features for software vulnerability detection. (18th December 2018)

• Record Type: Journal Article
• Authors: Ban, Xinbo; Liu, Shigang; Chen, Chao; Chua, Caslon
• Other Names: Wyrzykowski Roman guestEditor.; Szymanski Boleslaw guestEditor.; Pan Lei guestEditor.; Zhang Jun guestEditor.; Oliver Jonathan guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 19(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-12-18
• Subjects: deep learning -- security -- software vulnerability; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5103
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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.