剖析机器学习杀毒系统安全风险，揭示防御漏洞与改进方向
On the Security Risks of ML-based Malware Detection Systems: A Survey
- 按系统流程分阶段分析安全威胁，构建完整风险框架
- 通过案例研究发现跨阶段与单阶段攻击的新规律
- 适合安全研究人员和系统开发者参考防御策略
恶意软件持续威胁用户隐私与数据完整性。为应对该问题，基于机器学习的恶意软件检测（ML-based MD）系统应运而生。然而近年来其屡遭攻击，实际效果受损。尽管相关安全风险已受关注，多数研究仍局限于对抗性恶意样本，缺乏对实际安全风险的全面分析。本文基于CIA原则界定安全风险范畴，将ML-based MD系统分解为多个操作阶段，提出阶段式分类体系。利用该体系，总结各阶段攻防技术进展，揭示当前研究空白。进一步开展两项案例研究，结合阶段内与阶段间分析，获得新的实证洞察。据此，从阶段内外两个维度提出未来研究方向。
原文摘要 · Abstract (English)
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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