@inproceedings{2696b0e43d7141369ef9412fb0ea7ee2,
title = "Evading Deep Learning-Based Malware Detectors via Obfuscation: A Deep Reinforcement Learning Approach",
abstract = "Adversarial Malware Generation (AMG), the generation of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense. However, the majority of extant works offer subtle perturbations or additions to executable files and do not explore full-file obfuscation. In this study, we show that an open-source encryption tool coupled with a Reinforcement Learning (RL) framework can successfully obfuscate malware to evade state-of-the-art malware detection engines and outperform techniques that use advanced modification methods. Our results show that the proposed method improves the evasion rate from 27\%-49\% compared to widely-used state-of-the-art reinforcement learning-based methods.",
keywords = "Adversarial Malware Generation, Adversarial Malware Variants, Adversarial Robustness, Obfuscation, Reinforcement Learning",
author = "Brian Etter and Hu, \{James Lee\} and Mohammadreza Ebrahimi and Weifeng Li and Xin Li and Hsinchun Chen",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023 ; Conference date: 01-12-2023 Through 04-12-2023",
year = "2023",
doi = "10.1109/ICDMW60847.2023.00169",
language = "English",
series = "IEEE International Conference on Data Mining Workshops, ICDMW",
publisher = "IEEE Computer Society",
pages = "1313--1321",
editor = "Jihe Wang and Yi He and Dinh, \{Thang N.\} and Christan Grant and Meikang Qiu and Witold Pedrycz",
booktitle = "Proceedings - 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023",
address = "United States",
}