LKD-STNN: A Lightweight Malicious Traffic Detection Method for Internet of Things Based on Knowledge Distillation.

The purpose of malicious traffic detection and identification in the Internet of Things is to detect the intrusion of malicious traffic within the Internet of Things network into Internet of Things devices. Detection and identification play a key role in ensuring the security of the Internet of Things. At this time, great success has been achieved with deep learning in the field of malicious traffic detection and identification. However, due to resource limitations such as computation weaknesses and low edge network node storage capacity in the Internet of Things, a high-complexity model based on deep learning cannot be deployed and applied. In this paper, we propose a lightweight malicious traffic detection and recognition model named Lightweight Knowledge Distillation-Space Time Neural Network (LKD-STNN) based on knowledge distillation deep learning for the Internet of Things. We use knowledge distillation to build a lightweight student model by depth-wise separable convolution and BiLSTM to realize a lightweight student model and obtain multidimensional characteristic information. According to the characteristics of knowledge distillation, we propose an adaptive temperature function that can adaptively and dynamically change the temperature during the process of knowledge transfer so that different softening characteristics can be obtained during the training process. Then, the weight is updated by combining loss functions to improve the performance of the student model. The experimental results show that with the publicly available malicious traffic datasets for the Internet of Things, the ToN-IoT and IoT-23, our model not only reduces the complexity of the model and the number of model parameters to less than 1% of the teacher model but also reaches an accuracy of more than 98%, indicating that our model can be applied to the multiclassification identification of malicious traffic in the Internet of Things.