A systematic literature review and meta-analysis on artificial intelligence in penetration testing and vulnerability assessment

被引:34
|
作者
McKinnel, Dean Richard [1 ]
Dargahi, Tooska [1 ]
Dehghantanha, Ali [2 ]
Choo, Kim-Kwang Raymond [3 ]
机构
[1] Univ Salford, Dept Comp Sci, Manchester, Lancs, England
[2] Univ Guelph, Sch Comp Sci, Cyber Sci Lab, Guelph, ON, Canada
[3] Univ Texas San Antonio, Dept Informat Syst & Cyber Secur, San Antonio, TX 78249 USA
关键词
Penetration testing; Vulnerability assessment; Artificial intelligence; Systematic literature review; Machine learning; Meta-analysis; CHALLENGES; STATE;
D O I
10.1016/j.compeleceng.2019.02.022
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Vulnerability assessment (e.g., vulnerability identification and exploitation; also referred to as penetration testing) is a relatively mature industry, although attempting to keep pace with the diversity of computing and digital devices that need to be examined is challenging. Hence, there has been ongoing interest in exploring the potential of artificial intelligence to enhance penetration testing and vulnerability identification of systems, as evidenced by the systematic literature review performed in this paper. In this review, we focus only on empirical papers, and based on the findings, we identify a number of potential research challenges and opportunities, such as scalability and the need for real-time identification of exploitable vulnerabilities. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:175 / 188
页数:14
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