A Method of Abnormal Detection of Network Intrusion

Abstract

The invention discloses a network intrusion anomaly detection method. Aiming at the problem about high-dimensional colinearity of input features of a network intrusion anomaly detection model, firstly, the PCA (principal component analysis)-based feature extraction technology is adopted to extract potential features of input variables so as to eliminate the colinearity among the variables; then, the MI (mutual information)-based feature extraction technology is adopted to select the potential variables extracted through PCA so as to realize selection of mutually independent feature variables most related to the output category of the anomaly detection model; finally, the detection model is established based on the RVFL network with a higher learning rate through the feature input. With adoption of the network intrusion anomaly detection method, the defects that the extracted features of principal component analysis are uncorrelated to model output, and correlation among the variables needs to consider for mutual information based feature selection are counteracted, and the problem about high-dimensional colinearity of input features of the network intrusion anomaly detection model is effectively solved.

Description

technical field [0001] The invention relates to network security technology, in particular to a network intrusion anomaly detection method. Background technique [0002] With the wireless and mobile networks, and the gradual deepening and promotion of Industry 4.0, the information security situation is becoming increasingly severe, and the security protection of key infrastructure related to national security needs to be highly valued. Network intrusion detection refers to the discovery of behaviors that violate security policies or endanger system security in the system by collecting information such as operating systems, system programs, applications, and network packets. Existing network intrusion anomaly detection technologies include statistical analysis, pattern prediction, neural network, genetic algorithm, sequence matching and learning, immune system, norm-based, data mining, integrity checking and Bayesian techniques, etc. Aiming at the shortcoming that the curren...

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Problems solved by technology

However, the SVM model takes a lot of time to find the optimal solution, and its online recursive model replaces the optimal solution with a suboptimal solution, so it is difficult to apply the retraining method to achieve rapid update

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