We identify quantitative characteristics of responses to cyber compromises that can be learned from repeatable, systematic experiments. We model a vehicle equipped with an autonomous cyber-defense system and which also has some inherent physical resilience features. When attacked by malware, this ensemble of cyber-physical features (i.e., "bonware") strives to resist and recover from the performance degradation caused by the malware's attack. We propose parsimonious continuous models, and develop stochastic models to aid in quantifying systems' resilience to cyber attacks.

翻译：本文确定了可从可重复的系统性实验中学习的网络入侵响应量化特征。我们构建了一个配备自主网络防御系统且具备一定固有物理弹性特征的车辆模型。当遭受恶意软件攻击时，这种网络-物理特性组合（即"bonware"）致力于抵抗恶意软件攻击造成的性能退化并从中恢复。我们提出简约连续模型，并开发随机模型以帮助量化系统对网络攻击的弹性。