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    Cyber-Resilient Smart Cities: Detection of Malicious Attacks in Smart Grids

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    Type
    Article
    Authors
    Mohammadpourfard, Mostafa
    Khalili, Abdullah
    Genc, Istemihan
    Konstantinou, Charalambos cc
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2021-06-26
    Online Publication Date
    2021-06-26
    Print Publication Date
    2021-12
    Embargo End Date
    2023-10-23
    Submitted Date
    2021-03-23
    Permanent link to this record
    http://hdl.handle.net/10754/673024
    
    Metadata
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    Abstract
    A massive challenge for future cities is being environmentally sustainable by incorporating renewable energy resources (RES). At the same time, future smart cities need to support resilient environments against cyber-threats on their supported information and communication technologies (ICT). Therefore, the cybersecurity of future smart cities and their smart grids is of paramount importance, especially on how to detect cyber-attacks with growing uncertainties, such as frequent topological changes and RES of intermittent nature. Such raised uncertainties can cause a significant change in the underlying distribution of measurements and system states. In such an environment, historical measured data will not accurately exhibit the current network's operating point. Hence, future power grids’ dynamic behaviors within smart cities are much more complicated than the conventional ones, leading to incorrect classification of the new instances by the current attack detectors. In this paper, to address this problem, a long short-term memory (LSTM) recurrent neural network (RNN) is carefully designed by embedding the dynamically time-evolving power system's characteristics to accurately model the dynamic behaviors of modern power grids that are influenced by RES or system reconfiguration to distinguish natural smart grid changes and real-time attacks. The proposed framework's performance is evaluated using the IEEE 14-bus system using real-world load data with multiple attack cases such as attacks to the network after a line outage and combination of RES. Results confirm that the developed LSTM-based attack detection model has a generalization ability to catch modern power grids’ dynamic behaviors, excelling current traditional approaches in the designed case studies and achieves accuracy higher than 90% in all experiments.
    Citation
    Mohammadpourfard, M., Khalili, A., Genc, I., & Konstantinou, C. (2021). Cyber-Resilient Smart Cities: Detection of Malicious Attacks in Smart Grids. Sustainable Cities and Society, 75, 103116. doi:10.1016/j.scs.2021.103116
    Sponsors
    This work was supported by European Commission Horizon 2020 Marie Skłodowska-Curie Actions Cofund program and TÜBiTAK (Project Number: 120C080).
    Publisher
    Elsevier BV
    Journal
    Sustainable Cities and Society
    DOI
    10.1016/j.scs.2021.103116
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S221067072100398X
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.scs.2021.103116
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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