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

• Published in: International journal of electrical power & energy systems 2022-06, Vol.138, p.107954, Article 107954
• Main Authors: Tabar, Vahid Sohrabi, Ghassemzadeh, Saeid, Tohidi, Sajjad, Siano, Pierluigi
• Format: Article
• Language: English
• Subjects: Data mining; False data; Information security; Machine learning; Renewable smart grid
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2022.107954

•Modelling the false data injection attacks in renewable smart grids by using a bi-level process.•Simulating the behavior of attackers by utilizing a stochastic observation-action method.•Constructing an integrated online-offline framework for false data detection and correction.•Implementing a data mining process based on k-nearest neighbour and support vector machine.•Analyzing the data in the online framework according to a pre-secured sensor. 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.