TY - JOUR
T1 - Resilience of Smart Power Grids to False Pricing Attacks in the Social Network
AU - Tang, Daogui
AU - Fang, Yi Ping
AU - Zio, Enrico
AU - Ramirez-Marquez, Jose Emmanuel
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - Communication through social media can help engage end users to improve the efficiency of demand-side management in smart power grids. However, this opens a channel between the social network and the power grid through which malicious attackers can publish false information that can actually cause problems to the power grid. In this paper, we analyze this new problem by modeling a social network-coupled smart grid and investigating its vulnerability to false pricing attacks in the social network. The energy consumption profile based on social information is modeled as a consumption rescheduling problem, which aims to maximize the benefit of demand-side management. The false price spreading process is described by a multi-level influence propagation model, which takes into account the personalities of the end users. Different attack strategies are considered and the power operator's response is modeled. The residual ampacity of distribution lines and the expected energy not supplied are adopted to quantify the impacts of the attacks on the power system. To account for the stochastic characteristics of the influence propagation process, Monte Carlo simulation is utilized. The proposed modeling and analysis framework is applied on a modified IEEE 13 nodes test feeder and a notional social network. The vulnerability to attack is analyzed at both component and system levels.
AB - Communication through social media can help engage end users to improve the efficiency of demand-side management in smart power grids. However, this opens a channel between the social network and the power grid through which malicious attackers can publish false information that can actually cause problems to the power grid. In this paper, we analyze this new problem by modeling a social network-coupled smart grid and investigating its vulnerability to false pricing attacks in the social network. The energy consumption profile based on social information is modeled as a consumption rescheduling problem, which aims to maximize the benefit of demand-side management. The false price spreading process is described by a multi-level influence propagation model, which takes into account the personalities of the end users. Different attack strategies are considered and the power operator's response is modeled. The residual ampacity of distribution lines and the expected energy not supplied are adopted to quantify the impacts of the attacks on the power system. To account for the stochastic characteristics of the influence propagation process, Monte Carlo simulation is utilized. The proposed modeling and analysis framework is applied on a modified IEEE 13 nodes test feeder and a notional social network. The vulnerability to attack is analyzed at both component and system levels.
KW - Smart grids
KW - consumption rescheduling problem
KW - false pricing attacks
KW - residence demand-side management
KW - social networks
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U2 - 10.1109/ACCESS.2019.2923578
DO - 10.1109/ACCESS.2019.2923578
M3 - Article
AN - SCOPUS:85069055724
VL - 7
SP - 80491
EP - 80505
JO - IEEE Access
JF - IEEE Access
M1 - 8737944
ER -