Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/2743 2018 2018 Department of Computer Science and Engineering ,Karunya Institute of Technology and Sciences R. R. Department of Computer Science and Engineering ,Rajiv Gandhi College of Engineering, Anna University Sindhu. P. Department of Computer Science and Engineering ,Karunya Institute of Technology and Sciences G. Naveen Sundar Department of Computer Science and Engineering ,Karunya Institute of Technology and Sciences R. Venkatesan Smart grids, pivotal in modern energy distribution, confront a mounting cybersecurity threat landscape due to their increased connectivity. This study introduces a novel hybrid deep learning approach designed for robust intrusion detection, addressing the imperative to fortify the security of these critical infrastructures. Renamed as "Intrusion Detection for Smart Grid Using a Hybrid Deep Learning Approach," the study amalgamates Conv1D for spatial feature extraction, MaxPooling1D for dimensionality reduction, and GRU for modeling temporal dependencies. The research leverages the Edge-IIoTset Cyber Security Dataset, encompassing diverse layers of emerging technologies within smart grids and facilitating a nuanced understanding of intrusion patterns. Over 10 types of IoT devices and 14 attack categories contribute to the dataset's richness, enhancing the model's training and evaluation. The proposed hybrid model's architecture is detailed, emphasizing the synergy of convolutional and recurrent neural networks in addressing complex intrusion scenarios. This research not only contributes to the evolving field of intrusion detection in smart grids but also sets the stage for creating adaptive security systems. The convergence of a hybrid deep learning approach with a comprehensive cyber security dataset marks a significant stride towards fortifying smart grids against evolving cybersecurity threats. The proposed model achieves 98.20 percentage. 2024 2024 67 76 10.54216/FPA.160105 https://www.americaspg.com/articleinfo/3/show/2743