Embedded controllers, sensors, actuators, advanced metering infrastructure, etc. are cornerstone components of cyber-physical energy systems such as microgrids (MGs). Harnessing their monitoring and control functionalities, sophisticated schemes enhancing MG stability can be deployed. However, the deployment of `smart' assets increases the threat surface. Power systems possess mechanisms capable of detecting abnormal operations. Furthermore, the lack of sophistication in attack strategies can render them detectable since they blindly violate power system semantics. On the other hand, the recent increase of process-aware rootkits that can attain persistence and compromise operations in undetectable ways requires special attention. In this work, we investigate the steps followed by stealthy rootkits at the process level of control systems pre- and post-compromise. We investigate the rootkits' precompromise stage involving the deployment to multiple system locations and aggregation of system-specific information to build a neural network-based virtual data-driven model (VDDM) of the system. Then, during the weaponization phase, we demonstrate how the VDDM measurement predictions are paramount, first to orchestrate crippling attacks from multiple system standpoints, maximizing the impact, and second, impede detection blinding system operator situational awareness.
翻译:嵌入式控制器、传感器、感应器、动画机、先进的计量基础设施等是微电网等网络物理能源系统的基石组成部分。 利用它们的监测和控制功能,可以部署增强MG稳定性的尖端计划; 然而,部署“智能”资产会增加威胁表面; 电力系统拥有能够检测异常操作的机制; 此外,攻击战略不够精密,可能使其可以检测,因为它们盲目地违反动力系统语义学。 另一方面,最近增加能够以不可检测的方式实现持久性和妥协操作的进程根基需要特别注意。 在这项工作中,我们调查在控制系统前和组合后流程一级隐性根基所遵循的步骤。我们调查根基公司在多个系统地点部署和集集系统特定信息以建立基于神经网络的虚拟数据驱动模型(VDDM)之前的复杂阶段,我们展示了VDDM测量系统测量预测是如何在控制系统第二级和第二视角下达到最高水平的,阻碍对系统进行最大程度的检测,以及从多重定位到爆炸性攻击。