Hybrid Attention-Guided Deep Learning Framework for Enhanced Intrusion Detection in IoT Networks

Abstract

The mushrooming of Internet of Things (IoT) devices has provided an enormous attack surface, and thus IoT networks have become very susceptible to numerous cyber threats. Classical intrusion detection systems (IDS) tend to be ineffective in detecting sophisticated and changing attack patterns in such dynamic networks. This paper outlines a novel hybrid attention-guided deep learning paradigm for advanced intrusion detection in IoT networks. The system combines convolutional neural networks (CNNs) to extract features, recurrent neural networks (RNNs) with an attention mechanism to model temporal dependency, and a deep neural network (DNN) for prediction. The attention mechanism enables the model to pay attention to the most important features in the detection process, enhancing accuracy and minimizing false positives. The effectiveness of the presented framework is analyzed based on the NSL-KDD dataset, which proves its high performance compared to other state-of-the-art IDS methods based on detection accuracy, precision, recall, and F1-score. The outcomes show the effectiveness of the hybrid attention-guided deep learning model in securing IoT networks from intelligent cyberattacks.