Multi-view Representation Learning from Malware to Defend Against Adversarial Variants

James Lee Hu, Mohammadreza Ebrahimi, Weifeng Li, Xin Li, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Deep learning-based adversarial malware detectors have yielded promising results in detecting never-before-seen malware executables without relying on expensive dynamic behavior analysis and sandbox. Despite their abilities, these detectors have been shown to be vulnerable to adversarial malware variants - meticulously modified, functionality-preserving versions of original malware executables generated by machine learning. Due to the nature of these adversarial modifications, these adversarial methods often use a single view of malware executables (i.e., the binary/hexadecimal view) to generate adversarial malware variants. This provides an opportunity for the defenders (i.e., malware detectors) to detect the adversarial variants by utilizing more than one view of a malware file (e.g., source code view in addition to the binary view). The rationale behind this idea is that while the adversary focuses on the binary view, certain characteristics of the malware file in the source code view remain untouched which leads to the detection of the adversarial malware variants. To capitalize on this opportunity, we propose Adversarially Robust Multiview Malware Defense (ARMD), a novel multi-view learning framework to improve the robustness of DL-based malware detectors against adversarial variants. Our experiments on three renowned open-source deep learning-based malware detectors across six common malware categories show that ARMD is able to improve the adversarial robustness by up to seven times on these malware detectors.

Original languageEnglish (US)
Title of host publicationProceedings - 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022
EditorsK. Selcuk Candan, Thang N. Dinh, My T. Thai, Takashi Washio
PublisherIEEE Computer Society
Number of pages8
ISBN (Electronic)9798350346091
StatePublished - 2022
Event22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022 - Orlando, United States
Duration: Nov 28 2022Dec 1 2022

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259


Conference22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022
Country/TerritoryUnited States


  • Adversarial Machine Learning
  • Adversarial Malware Variants
  • Adversarial Robustness
  • Deep Learning-based Malware Detectors
  • Multi-View Learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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