Enhanced intrusion detection in smart grids using extended long short-term memory variants

Smart grid systems, which integrate traditional energy infrastructure with modern communication technologies, face significant cybersecurity challenges due to their dynamic architecture and continuous data exchange. The diversity and interconnection of devices increase vulnerability to malicious intrusions, highlighting the need for advanced and scalable detection methods. This study aims to develop an intrusion detection system (IDS) for smart grids by leveraging recent advances in deep learning, specifically enhanced variants of Long Short-Term Memory (LSTM)—xLSTM, sLSTM, and mLSTM. These sequence modeling architectures were adapted and fine-tuned within our IDS framework to capture complex spatio-temporal patterns and handle heterogeneous, high-dimensional data effectively. A comprehensive evaluation on two benchmark datasets, NSL-KDD and DNP3, demonstrates the robustness of the proposed approach. On the NSL- KDD, xLSTM, sLSTM, and mLSTM achieved accuracies of 98.16%, 98.55%, and 98.54%. On the more modern, protocol-specific DNP3 dataset, which represents real-world SCADA-focused attacks, the models maintained their superior performance, achieving accuracies of 99.50%, 99.33%, and 99.42%, respectively. The high and consistent accuracy across both datasets demonstrates the models' dependability and adaptability for intrusion detection in smart grid infrastructures. The study's targeted enhancement of LSTM-based architectures contributes a novel and effective approach to protecting critical intelligent systems from emerging cyber threats.