An Industrial Internet of Things-oriented Malicious Traffic Detection Network with Neural Network Partial Architecture Search

Abstract

To cope with the task of malicious traffic detection in high-complexity and high-security-demands industrial internet of things scenarios, this paper presents a new malicious traffic detection model deployable at edge computing nodes in the industrial internet of things scenarios along with the training method by combining the multi-head self-attention mechanism with neural network architecture partial search for the first time in this field along with a new, generalized linear multidimensional projection method. In addition, this paper presents the partially learnable embedding based on two-dimensional Gaussian distribution for the first time to capture the absolute position information. The experimental results show that this model is both lightweight and efficient, and the accuracy is much higher than previous studies with the same dataset.