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：我们识别了从可重复、系统性实验中习得、针对网络入侵响应的定量特征。我们建模了一辆配备自主网络防御系统并具有固有物理弹性特征的车辆。当受到恶意软件攻击时，这套网络物理特征集合（即"bonware"）致力于抵抗恶意软件攻击造成的性能下降并从中恢复。我们提出了简约的连续模型，并开发了随机模型，以帮助量化系统对网络攻击的弹性。