CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications
This work addresses a critical gap in cybersecurity by enabling better assessment of adversarial robustness for ensemble defenses, though it is incremental as it focuses on evaluation rather than new defense methods.
The paper tackles the problem of evaluating the robustness of ensemble defenses against adaptive attacks in cybersecurity by proposing the CARE platform, which provides a comprehensive evaluation framework to address unresolved questions about ensemble defense effectiveness.
Ensemble defenses, are widely employed in various security-related applications to enhance model performance and robustness. The widespread adoption of these techniques also raises many questions: Are general ensembles defenses guaranteed to be more robust than individuals? Will stronger adaptive attacks defeat existing ensemble defense strategies as the cybersecurity arms race progresses? Can ensemble defenses achieve adversarial robustness to different types of attacks simultaneously and resist the continually adjusted adaptive attacks? Unfortunately, these critical questions remain unresolved as there are no platforms for comprehensive evaluation of ensemble adversarial attacks and defenses in the cybersecurity domain. In this paper, we propose a general Cybersecurity Adversarial Robustness Evaluation (CARE) platform aiming to bridge this gap.