TY - GEN
T1 - Moving Target Defense for Robust Monitoring of Electric Grid Transformers in Adversarial Environments
AU - Sengupta, Sailik
AU - Basu, Kaustav
AU - Sen, Arunabha
AU - Kambhampati, Subbarao
N1 - Funding Information:
Acknowledgements. The research is supported in part by ONR grants N00014-16-1-2892, N00014-18-1-2442, N00014-18-1-2840, N00014-19-1-2119, AFOSR grant FA9550-18-1-0067, DARPA SAIL-ON grant W911NF-19-2-0006, and DARPA CHASE under Grant W912CG19-C-0003 (via IBM).
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Electric power grid components, such as high voltage transformers (HVTs), generating stations, substations, etc. are expensive to maintain and, in the event of failure, replace. Thus, regularly monitoring the behavior of such components is of utmost importance. Furthermore, the recent increase in the number of cyberattacks on such systems demands that such monitoring strategies should be robust. In this paper, we draw inspiration from work in Moving Target Defense (MTD) and consider a dynamic monitoring strategy that makes it difficult for an attacker to prevent unique identification of behavioral signals that indicate the status of HVTs. We first formulate the problem of finding a differentially immune configuration set for an MTD in the context of power grids and then propose algorithms to compute it. To find the optimal movement strategy, we model the MTD as a two-player game and consider the Stackelberg strategy. With the help of IEEE test cases, we show the efficacy and scalability of our proposed approaches.
AB - Electric power grid components, such as high voltage transformers (HVTs), generating stations, substations, etc. are expensive to maintain and, in the event of failure, replace. Thus, regularly monitoring the behavior of such components is of utmost importance. Furthermore, the recent increase in the number of cyberattacks on such systems demands that such monitoring strategies should be robust. In this paper, we draw inspiration from work in Moving Target Defense (MTD) and consider a dynamic monitoring strategy that makes it difficult for an attacker to prevent unique identification of behavioral signals that indicate the status of HVTs. We first formulate the problem of finding a differentially immune configuration set for an MTD in the context of power grids and then propose algorithms to compute it. To find the optimal movement strategy, we model the MTD as a two-player game and consider the Stackelberg strategy. With the help of IEEE test cases, we show the efficacy and scalability of our proposed approaches.
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U2 - 10.1007/978-3-030-64793-3_13
DO - 10.1007/978-3-030-64793-3_13
M3 - Conference contribution
AN - SCOPUS:85098252988
SN - 9783030647926
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 241
EP - 253
BT - Decision and Game Theory for Security - 11th International Conference, GameSec 2020, Proceedings
A2 - Zhu, Quanyan
A2 - Baras, John S.
A2 - Poovendran, Radha
A2 - Chen, Juntao
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th Conference on Decision and Game Theory for Security, GameSec 2020
Y2 - 28 October 2020 through 30 October 2020
ER -