On the Security Risks of ML-based Malware Detection Systems: A Survey

• URL: http://arxiv.org/abs/2505.10903v1
• Date: Fri, 16 May 2025 06:15:31 GMT
• Title: On the Security Risks of ML-based Malware Detection Systems: A Survey
• Authors: Ping He, Yuhao Mao, Changjiang Li, Lorenzo Cavallaro, Ting Wang, Shouling Ji,
• Abstract summary: Malware presents a persistent threat to user privacy and data integrity.<n>To combat this, machine learning-based (ML-based) malware detection (MD) systems have been developed.<n>These systems have increasingly been attacked in recent years, undermining their effectiveness in practice.
• Score: 40.831924021306506
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Malware presents a persistent threat to user privacy and data integrity. To combat this, machine learning-based (ML-based) malware detection (MD) systems have been developed. However, these systems have increasingly been attacked in recent years, undermining their effectiveness in practice. While the security risks associated with ML-based MD systems have garnered considerable attention, the majority of prior works is limited to adversarial malware examples, lacking a comprehensive analysis of practical security risks. This paper addresses this gap by utilizing the CIA principles to define the scope of security risks. We then deconstruct ML-based MD systems into distinct operational stages, thus developing a stage-based taxonomy. Utilizing this taxonomy, we summarize the technical progress and discuss the gaps in the attack and defense proposals related to the ML-based MD systems within each stage. Subsequently, we conduct two case studies, using both inter-stage and intra-stage analyses according to the stage-based taxonomy to provide new empirical insights. Based on these analyses and insights, we suggest potential future directions from both inter-stage and intra-stage perspectives.

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