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dc.contributor.authorZhang, Bing
dc.contributor.authorLi, Jingyue
dc.contributor.authorren, jiadong
dc.contributor.authorHuang, Guoyan
dc.date.accessioned2021-11-08T06:55:12Z
dc.date.available2021-11-08T06:55:12Z
dc.date.created2021-11-05T16:32:58Z
dc.date.issued2022
dc.identifier.citationACM Computing Surveys. 2022, 54 (9), 1-35.en_US
dc.identifier.issn0360-0300
dc.identifier.urihttps://hdl.handle.net/11250/2828241
dc.description.abstractMost 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.en_US
dc.language.isoengen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.titleEfficiency and Effectiveness of Web Application Vulnerability Detection Approaches: A Reviewen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 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.en_US
dc.source.pagenumber1-35en_US
dc.source.volume54en_US
dc.source.journalACM Computing Surveysen_US
dc.source.issue9en_US
dc.identifier.doihttps://doi.org/10.1145/3474553
dc.identifier.cristin1951914
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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