Deep Cost-Sensitive Kernel Machine for binary software vulnerability detection

Tuan Nguyen, Trung Le, Khanh Nguyen, Olivier de Vel, Paul Montague, John Grundy, Dinh Phung

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2 Citations (Scopus)


Owing to the sharp rise in the severity of the threats imposed by software vulnerabilities, software vulnerability detection has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security. Software vulnerability detection can be carried out at the source code or binary level. However, the latter is more impactful and practical since when using commercial software, we usually only possess binary software. In this paper, we leverage deep learning and kernel methods to propose the Deep Cost-sensitive Kernel Machine, a method that inherits the advantages of deep learning methods in efficiently tackling structural data and kernel methods in learning the characteristic of vulnerable binary examples with high generalization capacity. We conduct experiments on two real-world binary datasets. The experimental results have shown a convincing outperformance of our proposed method over the baselines.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication24th Pacific-Asia Conference, PAKDD 2020 Singapore, May 11–14, 2020 Proceedings, Part II
EditorsHady W. Lauw, Raymond Chi-Wing Wong, Ee-Peng Lim, Alexandros Ntoulas, See-Kiong Ng, Sinno Jialin Pan
Place of PublicationCham Switzerland
Number of pages14
ISBN (Electronic)9783030474362
ISBN (Print)9783030474355
Publication statusPublished - 2020
EventPacific-Asia Conference on Knowledge Discovery and Data Mining 2020 - Singapore, Singapore
Duration: 11 May 202014 May 2020
Conference number: 24th (Website) (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 2020
Abbreviated titlePAKDD 2020
Internet address

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