Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model

• URL: http://arxiv.org/abs/2012.07994v1
• Date: Mon, 14 Dec 2020 22:54:53 GMT
• Title: Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model
• Authors: Mohammadreza Ebrahimi, Ning Zhang, James Hu, Muhammad Taqi Raza, Hsinchun Chen
• Abstract summary: MalRNN is a novel approach to automatically generate evasive malware variants without restrictions. MalRNN effectively evades three recent deep learning-based malware detectors and outperforms current benchmark methods.
• Score: 11.701290164823142
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anti-malware engines are the first line of defense against malicious software. While widely used, feature engineering-based anti-malware engines are vulnerable to unseen (zero-day) attacks. Recently, deep learning-based static anti-malware detectors have achieved success in identifying unseen attacks without requiring feature engineering and dynamic analysis. However, these detectors are susceptible to malware variants with slight perturbations, known as adversarial examples. Generating effective adversarial examples is useful to reveal the vulnerabilities of such systems. Current methods for launching such attacks require accessing either the specifications of the targeted anti-malware model, the confidence score of the anti-malware response, or dynamic malware analysis, which are either unrealistic or expensive. We propose MalRNN, a novel deep learning-based approach to automatically generate evasive malware variants without any of these restrictions. Our approach features an adversarial example generation process, which learns a language model via a generative sequence-to-sequence recurrent neural network to augment malware binaries. MalRNN effectively evades three recent deep learning-based malware detectors and outperforms current benchmark methods. Findings from applying our MalRNN on a real dataset with eight malware categories are discussed.

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