A novel deep learning-based feature selection model for improving the static analysis of vulnerabili...
A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection
About this item
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Author / Creator
Publisher
London: Springer London
Journal title
Language
English
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Publication information
Publisher
London: Springer London
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Scope and Contents
Contents
The automatic detection of software vulnerabilities is considered a complex and common research problem. It is possible to detect several security vulnerabilities using static analysis (SA) tools, but comparatively high false-positive rates are observed in this case. Existing solutions to this problem depend on human experts to identify functionali...
Alternative Titles
Full title
A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_proquest_journals_2585227795
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2585227795
Other Identifiers
ISSN
0941-0643
E-ISSN
1433-3058
DOI
10.1007/s00521-021-06047-x
