Multiscale residual temporal convolutional networks-based intrusion detection model in Internet of things

Intrusion detection can actively identify Internet of things (IoT) traffic attacks, which is an important measure to maintain IoT security. Therefore, multiscale residual temporal convolutional networks-based intrusion detection model (MRID) was proposed. In MRID, a multiscale residual temporal convolutional module was utilized to enhance the network capability in learning spatiotemporal representations. An improved traffic attention mechanism was introduced to estimate the importance score that helps the model to concentrate on important information during leaning. The proposed MRID was easily integrated into a fog-enabled IoT to offer efficient real-time intrusion detection. Finally, empirical evaluations on two recent datasets (CICIDS2017 and CSE-CIC-IDS2018) were conducted, demonstrating that MRID improved the efficiency of intrusion detection and increased the robustness of model while maintaining computational efficiency.