Cyber-Physical Risk Assessment for False Data Injection Attacks Considering Moving Target Defences

In this paper, we examine the factors that influence the success of false data injection (FDI) attacks in the context of both cyber and physical styles of reinforcement. Existing research considers the FDI attack in the context of the ability to change a measurement in a static system only. However, successful attacks will require first intrusion into a system followed by construction of an attack vector that can bypass bad data detection to cause a consequence (such as overloading). Furthermore, the recent development of moving target defences (MTD) introduces dynamically changing system topology, which is beyond the capability of existing research to assess. In this way, we develop a full service framework for FDI risk assessment. The framework considers both the costs of system intrusion via a weighted graph assessment in combination with a physical, line overload-based vulnerability assessment under the existence of MTD. We present our simulations on a IEEE 14-bus system with an overlain RTU network to model the true risk of intrusion. The cyber model considers multiple methods of entry for the FDI attack including meter intrusion, RTU intrusion and combined style attacks. Post-intrusion, our physical reinforcement model analyses the required level of topology divergence to protect against a branch overload from an optimised attack vector. The combined cyber and physical index is used to represent the system vulnerability against FDIA.