AMVG: Adaptive Malware Variant Generation Framework Using Machine Learning

Jusop Choi, Dongsoon Shin, Hyoungshick Kim, Jason Seotis, Jin Hong

Research output: Chapter in Book/Conference paperConference paper

1 Citation (Scopus)

Abstract

There are advances in detecting malware using machine learning (ML), but it is still a challenging task to detect advanced malware variants (e.g., polymorphic and metamorphic variations). To detect such variants, we first need to understand the methods used to generate them to bypass the detection
methods. In this paper, we introduce an adaptive malware variant generation (AMVG) framework to study bypassing malware detection methods efficiently. The AMVG framework uses ML (e.g., genetic algorithm (GA)) to generate malware variants that satisfy specific detection criteria. The use of GA automates the
malware variant generations with appropriate modules to handle various input formats. For the experiment, we use malware samples retrieved from theZoo, a collection of malware samples1. The results show that we can automatically generate malware variants that satisfy varying detection criteria in a practical
amount of time, as well as showing the capabilities to handle different input formats.
Original languageEnglish
Title of host publicationProceedings of the 24th IEEE Pacific Rim International Symposium on Dependable Computing
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages11
ISBN (Electronic)139781728149615
DOIs
Publication statusPublished - 2019
Event24th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC 2019) - Rihga Royal Hotel Kyoto, Kyoto, Japan
Duration: 1 Dec 20193 Dec 2019
http://prdc.dependability.org/PRDC2019/

Conference

Conference24th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC 2019)
Abbreviated titlePRDC 2019
CountryJapan
CityKyoto
Period1/12/193/12/19
Internet address

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