DeeDP: vulnerability detection and patching based on deep learning

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dc.contributor.authorSavchenko, A.
dc.contributor.authorFokin, O.
dc.contributor.authorChernousov, A.
dc.contributor.authorSinelnikova, O.
dc.contributor.authorOsadchyi, S.
dc.date.accessioned2020-10-15T15:02:50Z
dc.date.available2020-10-15T15:02:50Z
dc.date.issued2020
dc.description.abstractenWe present the DeeDP system for automatic vulnerabilities detection and patch providing. DeeDP allows to detect vulnerabilities in C/C++ source code and generate patch for fixing detected issue. This system uses deep learning methods to organize rules for deciding whether a code fragment is vulnerable. Patch generation processes can be performed based on neural network and rule-based approaches. The system uses the abstract syntax tree (AST) representations of the source code fragments. We have tested effectiveness of our approach on different open source projects. For example, Microsoft/Terminal (https://github.com/microsoft/Terminal) was analyzed with DeeDP: our system detected security issue and generated patch which was successfully approved and applied by Microsoft maintainers.uk
dc.format.pagerangePp. 56-62uk
dc.identifier.citationDeeDP: vulnerability detection and patching based on deep learning / A. Savchenko, O. Fokin, A. Chernousov, O. Sinelnikova, S. Osadchyi // Theoretical and Applied Cybersecurity : scientific journal. – 2020. – Vol. 2, Iss. 1. – Pp. 56–62. – Bibliogr.: 21 ref.uk
dc.identifier.doihttps://doi.org/10.20535/tacs.2664-29132020.1.209465
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/36794
dc.language.isoenuk
dc.publisherIgor Sikorsky Kyiv Polytechnic Instituteuk
dc.publisher.placeKyivuk
dc.sourceTheoretical and Applied Cybersecurity : scientific journal, 2020, Vol. 2, No. 1uk
dc.subjectvulnerability detectionuk
dc.subjectpatch generationuk
dc.subjectdeep learninguk
dc.subject.udc004uk
dc.titleDeeDP: vulnerability detection and patching based on deep learninguk
dc.typeArticleuk

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Theoretical and Applied Cybersecurity: scientific journal, Vol. 2, No. 1