A convolutional transformation network for malware classification

D.-L. Vu, T.-K. Nguyen, T.V. Nguyen, T.N. Nguyen, F. Massacci, P.H. Phung

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

© 2019 IEEE.Modern malware evolves various detection avoidance techniques to bypass the state-of-the-art detection methods. An emerging trend to deal with this issue is the combination of image transformation and machine learning techniques to classify and detect malware. However, existing works in this field only perform simple image transformation methods that limit the accuracy of the detection. In this paper, we introduce a novel approach to classify malware by using a deep network on images transformed from binary samples. In particular, we first develop a novel hybrid image transformation method to convert binaries into color images that convey the binary semantics. The images are trained by a deep convolutional neural network that later classifies the test inputs into benign or malicious categories. Through the extensive experiments, our proposed method surpasses all baselines and achieves 99.14% in terms of accuracy on the testing set.
Original languageEnglish
Title of host publicationProceedings - 2019 6th NAFOSTED Conference on Information and Computer Science, NICS 2019
EditorsV.N. Quoc Bao, P.M. Quang, H. Van Hoa
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages234-239
ISBN (Electronic)9781728151632
ISBN (Print)9781728151632
DOIs
Publication statusPublished - 1 Dec 2019
Externally publishedYes
Event6th NAFOSTED Conference on Information and Computer Science, NICS 2019 - Hanoi, Viet Nam
Duration: 12 Dec 201913 Dec 2019

Publication series

NameProceedings - 2019 6th NAFOSTED Conference on Information and Computer Science, NICS 2019

Conference

Conference6th NAFOSTED Conference on Information and Computer Science, NICS 2019
CountryViet Nam
CityHanoi
Period12/12/1913/12/19

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