A New Intrusion Detection Method Based on Industrial Internet

Abstract

With the rapid development of the Industrial Internet, the security risks of the Industrial Internet will soon be exposed. In view of the low accuracy of the existing intrusion detection algorithms, the difficulty in adapting to the industrial Internet networking mode, and the imbalance of massive data, this paper proposes a capsule-based method. The network intrusion detection method, this method first refers to the DRN structure, introduces the residual block as the main capsule layer to extract high-quality feature maps, then uses the dynamic routing algorithm to cluster the features, and uses the Adam algorithm to optimize the learning in backpropagation rate to make the detection model stable and fast. In terms of convergence, the detection accuracy rate in the simulation test using the gas pipeline data set in this paper reaches 99.28%, and it is more robust to massive unbalanced data. The experimental results show that this method can better meet the current industrial internet security needs.