5.3.2: Graph Theoretic Approaches for Cyber Physical Security in Networks
Abstract
The scale and complexity of modern power distribution networks have increased significantly over the years. The once centralized control structure has paved way for a more distributed control and operations framework. Establishments such as U. S. Army, Industries etc. have invested in creating their own power distribution networks such as Tactical microgrids, Industrial microgrids, etc. to improve reliability and efficiency. The key to reliable operations of these decentralized microgrids is the ability of continuous monitoring, fast decision making and control signaling to avoid any anomalies (blackouts, brownouts, etc.) in the grid due to frequency or voltage fluctuations. The robustness of decision making relies on the integrity of the current state of the grid as estimated from the monitoring system. Standard open Internet based communication protocols are being widely adopted in such distributed networks for monitoring the assets (energy generation resources, loads, delivery infrastructure, etc.). Thus, the integrity of the state estimation is coming under immense pressure due to the large number of entry points and the increasing openness of the networks. Each new entry point provides cyber-attackers with an opportunity to inject malicious data into the monitoring system and compromise the integrity of the state estimation. These attacks are very hard to detect as the state estimation can no longer be relied upon. Thus, the attacker can stealthily bleed the grid out of energy production resources over a period or can time the attacks to cause major disruptions during critical periods without the knowledge of the grid operator. This project, a collaboration between the University of Southern California and Florida International University, will develop algorithmic techniques based on graph theoretic formulations to defend critical power distribution networks such as tactical microgrids. The project will develop data driven techniques to detect which assets are under attack - specifically data spoofing attacks in which the data obtained by the monitoring system of an asset is compromised; and quantify the threat to the grid that such attacks pose to facilitate the grid operators in triaging the assets for recovery. The project will also develop subgraph (e.g. modified Steiner trees) based protection schemes to protect critical grid assets and maintain situational awareness. We will gauge the success of our work by evaluating the accuracy of our models, the cost of our protection schemes and the penalty due to failure in protection or misidentification of critical assets.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 25, 2019
- Source ID
- W911NF1910362
Entities
People
- Viktor K. Prasanna
Organizations
- Army Contracting Command
- United States Army
- University of Southern California