Proposed algorithm for smart grid DDoS detection based on deep learning

• Published in: Neural networks Vol. 159; pp. 175 - 184
• Main Authors: Diaba, Sayawu Yakubu, Elmusrati, Mohammed
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.02.2023
• Subjects: Algorithms; Benchmarking; Canada; Convolutional neural network; Deep Learning; Distributed denial of service; Gated recurrent unit; Intrusion detection; Neural Networks, Computer; Smart grid; State estimation; Canada; Gated recurrent unit; State estimation; Smart grid; Intrusion detection; Convolutional neural network; Distributed denial of service
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2022.12.011

The Smart Grid’s objective is to increase the electric grid’s dependability, security, and efficiency through extensive digital information and control technology deployment. As a result, it is necessary to apply real-time analysis and state estimation-based techniques to ensure efficient controls are implemented correctly. These systems are vulnerable to cyber-attacks, posing significant risks to the Smart Grid’s overall availability due to their reliance on communication technology. Therefore, effective intrusion detection algorithms are required to mitigate such attacks. In dealing with these uncertainties, we propose a hybrid deep learning algorithm that focuses on Distributed Denial of Service attacks on the communication infrastructure of the Smart Grid. The proposed algorithm is hybridized by the Convolutional Neural Network and the Gated Recurrent Unit algorithms. Simulations are done using a benchmark cyber security dataset of the Canadian Institute of Cybersecurity Intrusion Detection System. According to the simulation results, the proposed algorithm outperforms the current intrusion detection algorithms, with an overall accuracy rate of 99.7%. •A proposed algorithm for Intrusion Detection in Smart Grid.•The algorithm is hybridized by CNN and GRU models.•Performance of the proposed algorithm is compared to the GRU, CNN, and LSTM.•An accuracy rate of 99.97% is achieved by the proposed algorithm.