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Efficiency and Effectiveness of Web Application Vulnerability Detection Approaches: A Review

Zhang, Bing; Li, Jingyue; ren, jiadong; Huang, Guoyan
Peer reviewed, Journal article
Accepted version
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URI
https://hdl.handle.net/11250/2828241
Date
2022
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  • Institutt for datateknologi og informatikk [6323]
  • Publikasjoner fra CRIStin - NTNU [34929]
Original version
ACM Computing Surveys. 2022, 54 (9), 1-35.   https://doi.org/10.1145/3474553
Abstract
Most existing surveys and reviews on web application vulnerability detection (WAVD) approaches focus on comparing and summarizing the approaches’ technical details. Although some studies have analyzed the efficiency and effectiveness of specific methods, there is a lack of a comprehensive and systematic analysis of the efficiency and effectiveness of various WAVD approaches. We conducted a systematic literature review (SLR) of WAVD approaches and analyzed their efficiency and effectiveness. We identified 105 primary studies out of 775 WAVD articles published between January 2008 and June 2019. Our study identified 10 categories of artifacts analyzed by the WAVD approaches and 8 categories of WAVD meta-approaches for analyzing the artifacts. Our study’s results also summarized and compared the effectiveness and efficiency of different WAVD approaches on detecting specific categories of web application vulnerabilities and which web applications and test suites are used to evaluate the WAVD approaches. To our knowledge, this is the first SLR that focuses on summarizing the effectiveness and efficiencies of WAVD approaches. Our study results can help security engineers choose and compare WAVD tools and help researchers identify research gaps.
Publisher
Association for Computing Machinery (ACM)
Journal
ACM Computing Surveys
Copyright
© ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.

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