TS-Net: Dual-Channel IoT Intrusion Detection with Temporal and Spatial Modeling

The rapid growth of the Internet of Things (IoT) has introduced significant security challenges, particularly in detecting intrusions within complex IoT networks. This paper presents TS-Net, a robust dual-channel model that combines temporal and spatial feature learning to enhance IoT intrusion detection. By partitioning network traffic into temporal and spatial features, TS-Net processes them through separate channels. The temporal channel utilizes Bidirectional Gated Recurrent Units (BiGRU) paired with a self-attention mechanism to capture dynamic sequential dependencies, while the spatial channel employs multi-scale dilated convolutions to extract patterns from varying spatial perspectives. These two channels are then fused to improve the model accuracy in detecting anomalous traffic. Experimental results on three publicly available datasets demonstrate that TS-Net outperforms existing intrusion detection models, achieving higher precision, recall, and F1-scores, demonstrating its effectiveness in addressing the unique security needs of IoT networks.