Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations

• URL: http://arxiv.org/abs/2110.11987v1
• Date: Fri, 22 Oct 2021 18:12:00 GMT
• Title: Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations
• Authors: Marek Galovic, Branislav Bosansky, Viliam Lisy
• Abstract summary: malware authors are trying to avoid detection by generating randoms and/or modifying useds with new versions of the malware. Our approach learns latent representations of input strings in an unsupervised fashion. We use these examples to improve the classifier's robustness by training on the generated adversarial set of strings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In malware behavioral analysis, the list of accessed and created files very often indicates whether the examined file is malicious or benign. However, malware authors are trying to avoid detection by generating random filenames and/or modifying used filenames with new versions of the malware. These changes represent real-world adversarial examples. The goal of this work is to generate realistic adversarial examples and improve the classifier's robustness against these attacks. Our approach learns latent representations of input strings in an unsupervised fashion and uses gradient-based adversarial attack methods in the latent domain to generate adversarial examples in the input domain. We use these examples to improve the classifier's robustness by training on the generated adversarial set of strings. Compared to classifiers trained only on perturbed latent vectors, our approach produces classifiers that are significantly more robust without a large trade-off in standard accuracy.

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