Lightweight Intrusion Detection Model of the Internet of Things with Hybrid Cloud-Fog Computing

Abstract

While promoting the development of the Internet of Things, cloud-fog hybrid computing faces severe information security risks. The intrusion detection system deployed in the fog node has lower latency but needs to be more lightweight. In response to the abovementioned problems, this paper proposes a lightweight intrusion detection model based on ConvNeXt-Sf. First, the two-dimensional structure of the latest computer vision model ConvNeXt is reduced to a one-dimensional sequence. Then, the design criteria of the lightweight computer vision model ShuffleNet V2 are used to improve ConvNeXt to make the latter more lightweight. Finally, the max-min normalization and label encoder are built into the data preprocessing model to convert the network traffic into a form conducive to ConvNeXt learning. The proposed model is evaluated on the TON-IoT and BoT-IoT datasets. The params of ConvNeXt-Sf are only 1.25% of that of ConvNeXt. Compared with the ConvNeXt, the ConvNeXt-Sf shortens the training time and prediction time by 82.63% and 56.48%, respectively, without reducing the learning capability and detection capability. Compared with the traditional models, the accuracy of the proposed model is increased by 6.18%, and the FAR is decreased by 4.49%. Compared with other lightweight models, the ShuffleNet V2 is better at making ConvNeXt lightweight.