An Intrusion Detection Model Based on AFSA-SVM for Petroleum Multi-Channel Industrial Control Network Communication

Abstract

To prevent viruses and zombie programs from affecting the secure communication of oil multi-channel industrial control networks, an intrusion detection model for oil multi-channel industrial control network communication based on AFSA-SVM is studied. Collect the communication status information of the oil multi-channel industrial control network. The communication status features of the oil multi-channel industrial control network are extracted from the collected information by using the foraging, clustering, and tail chasing behaviors of the artificial fish swarm algorithm (AFSA). The weighted information gain method is used to reduce the extracted features, take the reduced features as the input, and use the support vector machine (SVM) to realize the intrusion detection of the oil multi-channel industrial control network communication. The experimental results show that the communication state features extracted from the model are representative; after feature reduction, the weighted error measure of intrusion detection is significantly reduced; under different intrusion intensities, the single intrusion and mixed intrusion detection effects of the model are excellent, and the intrusion destination IP address can be obtained.