Renewable energies are extensively utilized in smart grids. Due to the widespread use of information and communication technologies in such networks, their security has become a critical issue. This paper aims to enhance the information security of renewable smart grids under cyber-physical attacks. In this regard, it is assumed that the non-legitimate agents manipulate the data of solar and wind sensors to deteriorate the safe operation. Here, a stochastic real-time procedure based on the observation-action method is utilized to simulate the behavior of attackers. Then, to improve the security and mitigate the impact of such a vulnerability, an integrated framework composed of offline and online units is designed. To construct the offline framework, a data mining process including k-nearest neighbour and support vector machine algorithms is implemented based on real historical data. Furthermore, the online framework tracks the real-time data according to a sensor pre-secured by a firewall. The results show that the proposed framework is capable to relieve the influence of cyber-physical attacks where at least 79% of success rate will be achievable under simultaneous false data injection attacks.

Enhancing information security of renewable smart grids by utilizing an integrated online-offline framework

Siano P.
2022

Abstract

Renewable energies are extensively utilized in smart grids. Due to the widespread use of information and communication technologies in such networks, their security has become a critical issue. This paper aims to enhance the information security of renewable smart grids under cyber-physical attacks. In this regard, it is assumed that the non-legitimate agents manipulate the data of solar and wind sensors to deteriorate the safe operation. Here, a stochastic real-time procedure based on the observation-action method is utilized to simulate the behavior of attackers. Then, to improve the security and mitigate the impact of such a vulnerability, an integrated framework composed of offline and online units is designed. To construct the offline framework, a data mining process including k-nearest neighbour and support vector machine algorithms is implemented based on real historical data. Furthermore, the online framework tracks the real-time data according to a sensor pre-secured by a firewall. The results show that the proposed framework is capable to relieve the influence of cyber-physical attacks where at least 79% of success rate will be achievable under simultaneous false data injection attacks.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4804943
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