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Modernized electrical grid automated to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity.

Smart grid includes two interdependent infrastructures: power transmission and distribution network, and the supporting telecommunications network. Complex interactions among these infrastructures lead to new pathways for attack and failure propagation that are currently not well understood. This innovative project takes a holistic multilevel approach to understand and characterize the interdependencies between these two infrastructures, and devise mechanisms to enhance their robustness. Specifically, the project has four goals. The first goal is to understand the standardized smart grid communications protocols in depth and examine mechanisms to harden them. This is essential since the current protocols are notoriously easy to attack. The second goal is to ensure robustness in state estimation techniques since they form the basis for much of the analysis of smart grid. In particular, the project shall exploit a steganography-based approach to detect bad data and compromised devices. The third goal is to explore trust-based attack detection strategies that combine the secure state estimation with power flow models and software attestation to detect and isolate compromised components. The final goal is to study reconfiguration strategies that combine light-weight prediction models, stochastic decision processes, intentional islanding, and game theory techniques to mitigate the spreading of failures and the loss of load. A unique aspect of smart grid security that will be studied in this project is the critical importance of timeliness, and thus a tradeoff between effectiveness of the mechanisms and the overhead introduced. The project is expected to provide practical techniques for making the smart grid more robust against failures and attacks, and enable it to recover from large scale failures with less loss of capacity. The project will also train students in the multidisciplinary areas of power systems operation and design, networking protocols, and cyber-physical security.

Award Number
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Co-PIs
Simone Silvestri
Institution
Missouri Science and Technology
Lead PI
sajaldas
Performance Period
-
Sponsor
National Science Foundation
Co-PIs (text)
Mariesa Crow
Submitted by Anonymous on March 15th, 2016
Exploiting inherent physical structure of the CPS domains can lead to economically viable and efficient novel algorithms for providing performance, control, synchronization and an alternate approach to CPS security that does not rely solely on cryptography. In each of these systems, regardless of the current state of the network, in the presence of disturbances or adversarial inputs, there is a need to bring the system to desired state for performance and control of the network. This project presents one such novel approach by observing that the CPS applications including smartgrid, coordinating robotics, formation flights in UAV, and synchronization of biological systems including brain networks all exhibit a special physical structure, namely submodularity, with respect to the set of control actions. Submodularity is a diminishing returns property that enables the development of efficient algorithms with provable optimality guarantees and in many cases distributed versions that are locally implementable, and hence scalable. While it has been widely used in the machine learning and discrete optimization communities, the use of submodularity in the context of CPS is a fertile research area. This project initially applies submodularity in the context of smart grid and show how it can lead to greater system stability and attack resilience. By defining suitable metrics that capture the submodular structures underlying the physical dynamics, the researchers develop algorithms that eliminate the time-consuming and computationally expensive verification of control actions through simulation. The fundamental properties of synchronization, convergence, robustness, and attack-resilience considered in this effort have crosscutting applications to multiple CPS domains, which will benefit from the submodular approach that we will research and develop.
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Co-PIs
Radha Poovendran
Daniel Kirschen
Institution
University of Washington
Lead PI
Linda Bushnell
Performance Period
-
Sponsor
National Science Foundation
Linda Bushnell Submitted by Linda Bushnell on March 9th, 2016
Abstract: The use of robots in society could be expanded by using reinforcement learning (RL) to allow robots to learn and adapt to new situations on-line. RL is a paradigm for learning sequential decision making tasks, usually formulated as a Markov Decision Process (MDP). For an RL algorithm to be practical for robotic control tasks, it must learn in very few samples, while continually taking actions in real-time. In addition, the algorithm must learn efficiently in the face of noise, sensor/actuator delays, and continuous state features.
Submitted by Peter Stone on February 15th, 2016
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Emily  Wehby Submitted by Emily Wehby on February 10th, 2016
The 35th International Conference on Computer Safety, Reliability and Security (SAFECOMP2016) ABOUT SAFECOMP
Submitted by Anonymous on February 3rd, 2016
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