Kommoju C Sravanthi,Kotapuri Mercy Rosalina,



Load Redistribution Attacks,Bi-level Programming Problem,Probabilistic static game theory,


In the present scenario of cyber-physical power grid, DC State Estimation (SE) plays a key role in the secure and reliable operation of power systems. Advanced communication and information technological devices like Remote Terminal Units’/Phasor Measurement Units’ measurement values are more prone to false/bad attack vectors. Those typical attacks that target SE are called False Data Injection Attacks (FDIAs) which can bypass classical detection methods. Load Redistribution Attacks (LRAs) are practical FDIAs that aim active bus power injections and active line power flows. Immediate LRAs lead to severe load shedding whereas delayed ones lead to load shedding and line outages too. To find the most damaging LRA vector, a bi-level mathematical optimization problem is framed, which represents attacker and defender. An optimal defense strategy is found by obtaining the Nash equilibrium on solving a two-player (attacker and defender) static zero-sum game considering load shedding as the utility function. The approach is analyzed on IEEE-30 bus test system, and attack and defense probabilities at Nash equilibrium are obtained.


I. Bi, Suzhi, and Ying Jun Zhang. “Graphical methods for defense against falsedata
injection attacks on power system state estimation.” IEEE Transactions
on Smart Grid 5.3 (2014): 1216-1227.
II. Ding, Zhilu, Yingmeng Xiang, and Lingfeng Wang. “Quantifying the
influence of local load redistribution attack on power supply adequacy.” 2016
IEEE Power and Energy Society General Meeting (PESGM). IEEE, 2016.
III. Liu, Xuan, and Zuyi Li. “Local load redistribution attacks in power systems
with incomplete network information.” IEEE Transactions on Smart Grid 5.4
(2014): 1665-1676.
IV. Liu, Xuan, and Zuyi Li. “Local topology attacks in smart grids.” IEEE
Transactions on Smart Grid 8.6 (2016): 2617-2626.
V. Liu, Xuan, et al. “Cyber attacks against the economic operation of power
systems: A fast solution.” IEEE Transactions on Smart Grid 8.2 (2016):
VI. Liu, Yao, Peng Ning, and Michael K. Reiter. “False data injection attacks
against state estimation in electric power grids.” ACM Transactions on
Information and System Security (TISSEC) 14.1 (2011): 13.
VII. Sharma, Neelam. “Analysis of Lactate Dehydrogenase & ATPase activity in
fish, Gambusia affinis at different period of exposureto chlorpyrifos.”
International Journal 4.1 (2014): 98-100.
VIII. Shen, Yubin, Minrui Fei, and Dajun Du. “Cyber security study for power
systems under denial of service attacks.” Transactions of the Institute of
Measurement and Control 41.6 (2019): 1600-1614.
IX. Xiang, Yingmeng, and Lingfeng Wang. “A game-theoretic study of load
redistribution attack and defense in power systems.” Electric Power Systems
Research 151 (2017): 12-25.
X. Xiang, Yingmeng, et al. “Coordinated attacks against power grids: Load
redistribution attack coordinating with generator and line attacks.” 2015 IEEE
Power & Energy Society General Meeting. IEEE, 2015.

XI. Xiang, Yingmeng, et al. “Power system reliability evaluation considering
load redistribution attacks.” IEEE Transactions on Smart Grid 8.2 (2016):
XII. Xiang, Yingmeng, Lingfeng Wang, and Nian Liu. “A framework for
modeling load redistribution attacks coordinating with switching attacks.”
2017 IEEE Power & Energy Society General Meeting. IEEE, 2017.
XIII. Yang, Yingpeng, et al. “Man-in-the-middle attack test-bed investigating
cyber-security vulnerabilities in smart grid SCADA systems.” (2012): 138-
XIV. Yuan, Yanling, Zuyi Li, and Kui Ren. “Modeling load redistribution attacks
in power systems.” IEEE Transactions on Smart Grid 2.2 (2011): 382-390.
XV. Yuan, Yanling, Zuyi Li, and Kui Ren. “Quantitative analysis of load
redistribution attacks in power systems.” IEEE Transactions on Parallel and
Distributed Systems 23.9 (2012): 1731-1738.

View Download