Biblio

In view of the problem that the intrusion detection method based on One-Class Support Vector Machine (OCSVM) could not detect the outliers within the industrial data, which results in the decision function deviating from the training sample, an anomaly intrusion detection algorithm based on Robust Incremental Principal Component Analysis (RIPCA) -OCSVM is proposed in this paper. The method uses RIPCA algorithm to remove outliers in industrial data sets and realize dimensionality reduction. In combination with the advantages of OCSVM on the single classification problem, an anomaly detection model is established, and the Improved Particle Swarm Optimization (IPSO) is used for model parameter optimization. The simulation results show that the method can efficiently and accurately identify attacks or abnormal behaviors while meeting the real-time requirements of the industrial control system (ICS).

In order to improve the security of wireless network, an anomaly intrusion detection algorithm based on adaptive time-frequency feature decomposition is proposed. This paper analyzes the types and detection principles of wireless network intrusion detection, it adopts the information statistical analysis method to detect the network intrusion, constructs the traffic statistical analysis model of the network abnormal intrusion, and establishes the network intrusion signal model by combining the signal fitting method. The correlation matching filter is used to filter the network intrusion signal to improve the output signal-to-noise ratio (SNR), the time-frequency analysis method is used to extract the characteristic quantity of the network abnormal intrusion, and the adaptive correlation spectrum analysis method is used to realize the intrusion detection. The simulation results show that this method has high accuracy and strong anti-interference ability, and it can effectively guarantee the network security.