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In malware behavioral analysis, the list of accessed and created files veryoften indicates whether the examined file is malicious or benign. However,malware authors are trying to avoid detection by generating random filenamesand/or modifying used filenames with new versions of the malware. These changesrepresent real-world adversarial examples. The goal of this work is to generaterealistic adversarial examples and improve the classifier's robustness againstthese attacks. Our approach learns latent representations of input strings inan unsupervised fashion and uses gradient-based adversarial attack methods inthe latent domain to generate adversarial examples in the input domain. We usethese examples to improve the classifier's robustness by training on thegenerated adversarial set of strings. Compared to classifiers trained only onperturbed latent vectors, our approach produces classifiers that aresignificantly more robust without a large trade-off in standard accuracy.

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