发表机构
The University of British Columbia（不列颠哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出Chameleon框架，利用基于机器学习的代理在检测到攻击时替换受损组件，实现信息物理系统从内存破坏攻击中的自动恢复，在多种机器人车辆上验证了高保真度和低开销。
AI 中文摘要
信息物理系统（CPS）越来越多地部署在我们生活的各个方面，并可能通过内存破坏漏洞受到攻击，使攻击者能够劫持控制流并接管系统。现有技术主要侧重于检测此类攻击，但在检测到攻击时通过终止或暂停执行来响应，这对于用于安全关键任务的CPS是不可接受的，因为中断的任务可能造成灾难性后果。其他技术用简化的默认值替换受损的CPS组件，这会降低系统行为，或者在检测到攻击时重启系统。我们提出Chameleon，一种新颖的框架，用于使用基于机器学习（ML）的代理自动从内存破坏攻击中恢复CPS，这些代理在分区粒度上训练，几乎复制其原始分区的行为，但没有相同的内存破坏漏洞。在检测到攻击时，Chameleon用其训练好的代理替换受损分区。我们使用LLVM编译器实现了Chameleon，并在七个不同的机器人车辆（RV）上评估了其效率和有效性，包括模拟和真实车辆。我们发现Chameleon可以生成紧密近似原始分区的代理（平均R²=0.96），与先前方法不同，成功地从真实世界的内存破坏攻击中恢复系统，并在完成其任务的同时产生较低的性能和内存开销。
英文摘要
Cyber-physical systems (CPSs) can be compromised through memory corruption vulnerabilities, which can result in safety violations. Existing techniques mostly focus on detecting such attacks but respond by terminating or halting execution upon attack detection, which is not acceptable in CPSs as interrupted tasks can have catastrophic consequences. Other techniques replace compromised CPS components with simplified defaults that degrade system behavior, or reboot the system upon attack detection, which are not suitable for CPS deployed in safety-critical domains. We propose Chameleon, a novel framework for automatically recovering CPSs from memory corruption attacks using machine learning (ML)-based surrogates trained at compartment granularity that nearly replicate their original compartments' behavior but are implemented differently, and hence are unlikely to have the same memory corruption vulnerabilities. Upon attack detection, Chameleon replaces the compromised compartment with its trained ML surrogate. We implemented Chameleon using the LLVM compiler, and evaluated its efficiency and effectiveness on seven different robotic vehicles (RVs), including simulated and real ones. We found that Chameleon can generate surrogates that closely approximate the original compartments (with an average R$^2$=0.96), successfully recover the system despite real-world memory corruption attacks and complete their tasks while incurring low performance and memory overheads on real RVs.
