TY - JOUR
T1 - A Detection Mechanism against Load-Redistribution Attacks in Smart Grids
AU - Kaviani, Ramin
AU - Hedman, Kory W.
N1 - Funding Information:
Manuscript received July 30, 2019; revised February 26, 2020 and May 27, 2020; accepted July 9, 2020. Date of publication August 18, 2020; date of current version December 21, 2020. This work was supported by the National Science Foundation (NSF) Awards under Grant 1449080, has been implemented to fulfill a part of the project: “A Verifiable Framework for Cyber-Physical Attacks and Countermeasures in a Resilient Electric Power Grid.” Paper no. TSG-01102-2019. (Corresponding author: Ramin Kaviani.) The authors are with the School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85281 USA (e-mail: rkaviani@asu.edu; kory.hedman@asu.edu).
Publisher Copyright:
© 2020 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - This article presents a real-time non-probabilistic approach to detect load-redistribution (LR) attacks, which attempt to cause an overflow, in smart grids. Prior studies have shown that certain LR attacks can bypass traditional bad data detectors and remain undetectable, which implies that the presence of a reliable and intelligent detection mechanism is imperative. Therefore, in this study a detection mechanism is proposed based on the fundamental knowledge of the physics laws in electric grids. To do so, we leverage power systems domain insight to identify an underlying exploitable structure for the core problem of LR attacks, which enables the prediction of the attackers' behavior. Then, a fast greedy algorithm is presented to find the best attack vector and identify the most sensitive buses for critical transmission assets. Finally, a security index, which can be used in practice with minimal disruptions, is developed for each critical asset with respect to the identified best attack vector and sensitive buses. The proposed approach is applied to 2383-bus Polish test system to demonstrate the scalability and efficiency of the proposed algorithm.
AB - This article presents a real-time non-probabilistic approach to detect load-redistribution (LR) attacks, which attempt to cause an overflow, in smart grids. Prior studies have shown that certain LR attacks can bypass traditional bad data detectors and remain undetectable, which implies that the presence of a reliable and intelligent detection mechanism is imperative. Therefore, in this study a detection mechanism is proposed based on the fundamental knowledge of the physics laws in electric grids. To do so, we leverage power systems domain insight to identify an underlying exploitable structure for the core problem of LR attacks, which enables the prediction of the attackers' behavior. Then, a fast greedy algorithm is presented to find the best attack vector and identify the most sensitive buses for critical transmission assets. Finally, a security index, which can be used in practice with minimal disruptions, is developed for each critical asset with respect to the identified best attack vector and sensitive buses. The proposed approach is applied to 2383-bus Polish test system to demonstrate the scalability and efficiency of the proposed algorithm.
KW - Cyber-attack detection
KW - false data injection attack (FDIA)
KW - greedy algorithm
KW - linear programming (LP)
KW - load-redistribution attack detection
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U2 - 10.1109/TSG.2020.3017562
DO - 10.1109/TSG.2020.3017562
M3 - Article
AN - SCOPUS:85098330500
SN - 1949-3053
VL - 12
SP - 704
EP - 714
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 1
M1 - 9170599
ER -