Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers
This addresses security risks in ML-based malware classification systems that rely on crowdsourced data, representing a novel attack method rather than an incremental improvement.
The paper tackles the vulnerability of feature-based ML malware classifiers to clean-label backdoor poisoning attacks by using explainable ML techniques to guide trigger creation, demonstrating effective attacks across Windows PE, PDF, and Android datasets with preserved functionality and showing defenses are difficult.
Training pipelines for machine learning (ML) based malware classification often rely on crowdsourced threat feeds, exposing a natural attack injection point. In this paper, we study the susceptibility of feature-based ML malware classifiers to backdoor poisoning attacks, specifically focusing on challenging "clean label" attacks where attackers do not control the sample labeling process. We propose the use of techniques from explainable machine learning to guide the selection of relevant features and values to create effective backdoor triggers in a model-agnostic fashion. Using multiple reference datasets for malware classification, including Windows PE files, PDFs, and Android applications, we demonstrate effective attacks against a diverse set of machine learning models and evaluate the effect of various constraints imposed on the attacker. To demonstrate the feasibility of our backdoor attacks in practice, we create a watermarking utility for Windows PE files that preserves the binary's functionality, and we leverage similar behavior-preserving alteration methodologies for Android and PDF files. Finally, we experiment with potential defensive strategies and show the difficulties of completely defending against these attacks, especially when the attacks blend in with the legitimate sample distribution.