Multi-view Representation Learning from Malware to Defend Against
Adversarial Variants
- URL: http://arxiv.org/abs/2210.15429v1
- Date: Tue, 25 Oct 2022 22:25:50 GMT
- Title: Multi-view Representation Learning from Malware to Defend Against
Adversarial Variants
- Authors: James Lee Hu, Mohammadreza Ebrahimi, Weifeng Li, Xin Li, Hsinchun Chen
- Abstract summary: 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.
- Score: 11.45498656419419
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 \textit{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.
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