The electric grid is becoming increasingly cyber-physical with the addition of smart technologies, new communication interfaces, and automated grid-support functions. Because of this, it is no longer sufficient to only study the physical system dynamics, but the cyber system must also be monitored as well to examine cyber-physical interactions and effects on the overall system. To address this gap for both operational and security needs, cyber-physical situational awareness is needed to monitor the system to detect any faults or malicious activity. Techniques and models to understand the physical system (the power system operation) exist, but methods to study the cyber system are needed, which can assist in understanding how the network traffic and changes to network conditions affect applications such as data analysis, intrusion detection systems (IDS), and anomaly detection. In this paper, we examine and develop models of data flows in communication networks of cyber-physical systems (CPSs) and explore how network calculus can be utilized to develop those models for CPSs, with a focus on anomaly and intrusion detection. This provides a foundation for methods to examine how changes to behavior in the CPS can be modeled and for investigating cyber effects in CPSs in anomaly detection applications.

The U.S. Department of Energy's Office of Scientific and Technical Information

Power systems have been analyzed and studied as purely physical systems for a long time. Such efforts were critical to the establishment of the power grid as it is today. However, with the increased interest in the integration of renewable energy, the grid is experiencing more vulnerabilities to its operation, stability, and resiliency from the cyber realm. As such, it is crucial to understand the cyber-physical power system interdependencies. In this paper, we advance a Bayesian Network generation algorithm, called CyberDep. CyberDep quantifies cyber-physical interdependencies through conditional probability calculations and aids in analyzing bidirectional data flow dependencies and n-to-1 nodal connections between elements. CyberDep is implemented on a dataset of the cyber-physical emulation of the WSCC 9-bus system, which includes running physical, cyber, and cyber-physical disturbances on the system. The results showcase an improved interdependency quantification and visualization of the n-to-1 probabilistic relationships between the physical and cyber system components.