TY - JOUR
T1 - Cyberattacks with limited network information leading to transmission line overflow in cyber–physical power systems
AU - Khazaei, Javad
N1 - Funding Information:
This research was in part under support from the U.S. Department of Defense, Office of Naval Research (ONR) under Grant N00014-20-1-2397 and Penn State's center for security research and education (CSRE), USA 2020 homeland security grant.
Funding Information:
This research was in part under support from the U.S. Department of Defense, Office of Naval Research (ONR) under Grant N00014-20-1-2397 and Penn State’s center for security research and education (CSRE), USA 2020 homeland security grant.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/9
Y1 - 2021/9
N2 - Recent cyberattacks on the U.S. power grid revealed how vulnerable smart grids are against cyberattacks. Consequently, it is critical to study cyberattacks that can lead to cascading failures in power systems and develop models to realistically emulate attack scenarios that can bypass existing bad data detection algorithms. In this paper, a mixed-integer linear programming model is developed to accurately model load redistribution attacks via false data injections (FDIs) that are targeted to overflow multiple transmission lines while the attackers have limited access to network topology and information. Compared to the existing research on cyberattacks on transmission line overflow, the proposed model considers that the attackers might have limited access to all measurements and can only target a set of measurements in a region (area) instead of full access to the network information. Numerical results on IEEE 118-bus benchmark revealed the effectiveness of the proposed modeling in overflowing up to four transmission lines with incomplete network information and FDIs limited to 40% of nominal loads.
AB - Recent cyberattacks on the U.S. power grid revealed how vulnerable smart grids are against cyberattacks. Consequently, it is critical to study cyberattacks that can lead to cascading failures in power systems and develop models to realistically emulate attack scenarios that can bypass existing bad data detection algorithms. In this paper, a mixed-integer linear programming model is developed to accurately model load redistribution attacks via false data injections (FDIs) that are targeted to overflow multiple transmission lines while the attackers have limited access to network topology and information. Compared to the existing research on cyberattacks on transmission line overflow, the proposed model considers that the attackers might have limited access to all measurements and can only target a set of measurements in a region (area) instead of full access to the network information. Numerical results on IEEE 118-bus benchmark revealed the effectiveness of the proposed modeling in overflowing up to four transmission lines with incomplete network information and FDIs limited to 40% of nominal loads.
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U2 - 10.1016/j.segan.2021.100505
DO - 10.1016/j.segan.2021.100505
M3 - Article
AN - SCOPUS:85108879285
VL - 27
JO - Sustainable Energy, Grids and Networks
JF - Sustainable Energy, Grids and Networks
SN - 2352-4677
M1 - 100505
ER -
