Defending Deep Learning-Based Raw Malware Detectors Against Adversarial Attacks: A Sequence Modeling Approach

• 成果类型: Article
• 署名作者: Ebrahimi, Reza; Hu, James Lee; Zhang, Ning; Nunamaker Jr, Jay F.; Chen, Hsinchun
• 署名单位: State University System of Florida; University of South Florida; University of Arizona
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222; 1557-928X
• DOI: 10.1080/07421222.2025.2561384
• 发表日期: 2025-10-02
• 页码: 1118-1148
• 关键词: IT infrastructure; cyber defense; adversarial malware attacks; adversarial malware; sequence models; Independent Recurrent Neural Nets (IRNNs); Cybersecurity; cybercrime prevention; design science; Examples

摘要

Malware detectors are the first line of defense against cyber-attacks that damage Information Technology (IT) infrastructure. Recently, deep learning (DL)-based malware detectors have yielded breakthrough results in identifying unseen attacks without requiring feature engineering and expensive dynamic malware analysis in a sandbox. However, these detectors are susceptible to adversarial malware attacks. Emulating effective adversarial malware variants is instrumental in revealing the vulnerabilities of such systems and developing automated cyber defense. Current methods for launching such attacks often assume scenarios that require accessing insider knowledge about the architecture of the malware detector and/or cannot operate directly on raw malware files. We propose Adversarial Malware example Generation and Defense (AMGD), a novel framework to defend the detectors by automatically generating malware variants from raw executables without assuming any prior detector knowledge. AMGD is generalizable to multiple detectors as it can be trained on multiple malware detectors simultaneously. AMGD employs Independent Recurrent Neural Nets (IndRNNs) to offer a novel generative byte-level malware sequence model, named Mal-IndRNN, to evade DL-based malware detectors. Mal-IndRNN effectively evades three renowned DL-based malware detectors and outperforms benchmark methods. We utilize malware variants generated by Mal-IndRNN to improve the robustness of malware detectors against adversarial attacks on a real dataset. AMGD offers a practical approach to proactively accounting for the Artificial Intelligence (AI)-enabled adversary in the design and development phase of DL-based malware detectors rather than reactive measures after deployment.