How to Train your Antivirus: RL-based Hardening through the Problem-Space
This addresses the vulnerability of antivirus systems to adversarial malware, offering a practical defense with theoretical guarantees, though it is incremental in applying RL to a specific domain.
The paper tackled the problem of hardening a commercial antivirus's ML-based malware detection against adversarial evasion by introducing a Reinforcement Learning approach to generate feasible adversarial examples, achieving 0% Attack Success Rate after a few retraining iterations.
ML-based malware detection on dynamic analysis reports is vulnerable to both evasion and spurious correlations. In this work, we investigate a specific ML architecture employed in the pipeline of a widely-known commercial antivirus company, with the goal to harden it against adversarial malware. Adversarial training, the sole defensive technique that can confer empirical robustness, is not applicable out of the box in this domain, for the principal reason that gradient-based perturbations rarely map back to feasible problem-space programs. We introduce a novel Reinforcement Learning approach for constructing adversarial examples, a constituent part of adversarially training a model against evasion. Our approach comes with multiple advantages. It performs modifications that are feasible in the problem-space, and only those; thus it circumvents the inverse mapping problem. It also makes possible to provide theoretical guarantees on the robustness of the model against a particular set of adversarial capabilities. Our empirical exploration validates our theoretical insights, where we can consistently reach 0% Attack Success Rate after a few adversarial retraining iterations.