Smart Grid intrusion detection system based on AI techniques

Smart grids (SGs) are integral to modern utility systems, managing power generation, energy consumption, and communication networks. However, as these systems become increasingly interconnected, they are exposed to sophisticated cyber threats that can compromise their functionality and security. To address these challenges, this paper presents an AI-driven detection framework designed to significantly enhance cybersecurity in smart grids. The proposed system combining Recurrent Neural Networks (RNNs) with Support vector classifier to improve detection accuracy, recognition capabilities, and system robustness. The methodology comprises four main stages: (1) data preprocessing to ensure high-quality input for analysis, (2) traffic detection using RNNs to capture temporal patterns, (3) classification of traffic as normal or abnormal via support vector classifier (SVC), and (4) identification of specific attack types through another SVC for refined threat categorization. This integrated approach enables real-time detection of both known and emerging threats, focusing on minimizing false positives and maximizing detection precision. The system was evaluated on three comprehensive benchmark datasets: UNSW_NB15 and BoT-IoT, achieving an average accuracy of 100%. These results underscore the superiority of this AI-based solution over traditional intrusion detection systems, providing a robust and scalable framework for securing smart grids and other critical infrastructures.