Optimized IoT Security with Attention-Based Hybrid Deep Learning Approach

Maintaining strong network security in the quickly developing Internet of Things (IoT) space is crucial because IoT traffic is so varied and complicated. By focusing on the TON IoT dataset, this study introduces a novel method for intrusion detection in IoT networks. Bidirectional Gated Recurrent Units (Bi-GRU) were utilized in a deep learning architecture to capture temporal dependencies in the data, while Convolutional Neural Networks (CNN) were utilized for effective feature extraction. By including a Multi-Head Attention layer, the model was able to select and pay attention to important characteristics, which enhanced its ability to focus on important patterns. A hybrid feature selection approach incorporating MI, Lasso, and RFECV was employed to generate a refined feature set, maintaining critical information for improved model performance. With a 99.73% binary classification accuracy and a 99.68% multi-class classification accuracy, our work outperformed the state-of-the-art techniques. These findings highlight how well CNN, Bi-GRU, and Attention mechanisms work together to detect intricate intrusion patterns in IoT environments, which advances the security measures required to shield IoT networks from dynamic cyber attacks.