Adversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective

arXiv cs.LG / 4/27/2026

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Key Points

The paper argues that ML-based malware detectors remain highly vulnerable to adaptive adversaries that use reinforcement learning rather than one-shot attacks.
It introduces a robust defense framework that formulates defender–attacker interaction as bilevel optimization, treating malware generation and detection as an adversarial co-evolution process.
Experiments on the MAB-malware framework across three malware families (Mokes, Strab, DCRat) show that baseline classifiers and basic adversarial retraining can still be evaded with evasion rates up to 90%.
In contrast, the proposed bilevel optimization approach reduces evasion rates to roughly 0–1.89% and increases attacker query complexity, making successful evasion up to two orders of magnitude more costly.
The authors conclude that explicitly modeling the iterative attack–defense cycle is key to building malware detection systems resilient to evolving adversarial threats.

Abstract

Machine learning-based malware detectors are increasingly vulnerable to adversarial examples. Traditional defenses, such as one-shot adversarial training, often fail against adaptive attackers who use reinforcement learning to bypass detection. This paper proposes a robust defense framework based on bilevel optimization, explicitly modeling the strategic interaction between a defender and an attacker as an adversarial co-evolutionary process. We evaluate our approach using the MAB-malware framework against three distinct malware families: Mokes, Strab, and DCRat. Our experimental results demonstrate that while standard classifiers and basic adversarial retraining often remain vulnerable, showing evasion rates as high as 90 %, the proposed bilevel optimization approach consistently achieves near-total immunity, reducing evasion rates to 0 - 1.89 %. Furthermore, the iterative framework significantly increases the attacker's query complexity, raising the average cost of successful evasion by up to two orders of magnitude. These findings suggest that modeling the iterative cycle of attack and defense through bilevel optimization is essential for developing resilient malware detection systems capable of withstanding evolving adversarial threats.