Industrial Internet Intrusion Detection Based on Res-CNN-SRU

Abstract

Nowadays, the industrial Internet is developing rapidly, but at the same time it faces serious information security risks. At present, industrial Internet data generally have the problems of complex attack sample types, large numbers, and high feature dimensions. When training a model, the complexity and quantity of attack samples will result in a long detection time for the intrusion detection algorithm, which will fall short of the system’s real-time performance. Due to the high feature dimension of the data, shallow feature extraction will be unable to extract the data’s more significant features, which will render the model’s overall detection capacity insufficient. Aiming at the above problems, an industrial Internet intrusion detection method based on Res-CNN-SRU is proposed. This method not only considers the temporality of network traffic data but can also effectively capture the local features in the data. The dataset used in the experiment is the gas pipeline industry dataset proposed by Mississippi State University in 2014. Experiments show that the algorithm can effectively improve the recognition rate of the system and reduce the false-alarm rate. At the same time, the training time required for this method is also greatly shortened, and it can perform efficient intrusion detection on the industrial Internet.