Distributed denial-of-service (DDoS) on the smart grids based on VGG19 deep neural network and Harris Hawks optimization algorithm.

The integration of advanced technologies into the infrastructure of modern smart grids has revolutionized the efficiency and reliability of energy distribution systems. However, the increasing reliance on interconnected digital systems exposes smart grids to various cyber threats, with distributed denial-of-service (DDoS) attacks posing a significant risk. This paper presents an effective method for identifying smart grid DDoS attacks by introducing the use of the deep neural network VGG19 combined with the Harris Hawks Optimization Algorithm (HHO). The suggested approach uses the robust feature extraction capability of VGG19-DNN for network traffic pattern analysis to detect abnormal traffic flows indicative of DDoS attacks. These features are then optimized using the HHO to enhance accuracy and efficiency. The approach also utilizes a distributed architecture for real-time monitoring and response, enabling timely mitigating of DDoS threats without compromising smart grid performance. The efficacy of the proposed framework is evaluated through extensive simulations and experiments using real-world smart grid datasets. Results demonstrated that the proposed approach outperforms existing methods in terms of detection accuracy and computational efficiency. Moreover, the robustness of the proposed solution against different attack scenarios is analyzed, and its scalability for large-scale deployments is validated. A comprehensive framework for protecting smart grids from DDoS attacks is developed, enabling more robust resilience and security of critical energy infrastructures against increasingly sophisticated cyber threats.