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.

翻译：摘要：集成防御广泛应用于各类安全相关应用中，以提升模型性能与鲁棒性。这些技术的广泛采用也引发诸多问题：通用集成防御是否一定比单一模型更鲁棒？随着网络安全军备竞赛的推进，更强的自适应攻击是否会击败现有集成防御策略？集成防御能否同时抵御不同类型的攻击，并抵抗持续调整的自适应攻击？遗憾的是，由于网络安全领域缺乏用于全面评估集成对抗攻击与防御的平台，这些关键问题仍未得到解答。本文提出一个通用网络安全对抗鲁棒性评估（CARE）平台，旨在弥合这一空白。