Network intrusion detection method

Abstract

The invention discloses a network intrusion detection method. The network intrusion detection method includes: searching network data to construct a test network data set; performing feature extraction on the test network data set by utilizing a kernel principal component analysis method; constructing a training data set, putting the training data set into a support vector machine classifier for training; obtaining feature datasets, obtaining an optimal feature subset from the feature data set by using a genetic algorithm; utilizing a firefly swarm optimization algorithm to obtain the overalllocal optimal feature subset and the optimal support vector machine parameters from the optimal feature subset, processing the training data set according to the overall local optimal feature subset,and inputting the training data set into a support vector machine classifier for classification modeling to obtain a network intrusion detection model. According to the method, the simplicity and convenience of the algorithm are improved, abnormal data can be more effectively found from samples, the detection accuracy of network intrusion is effectively improved, the missing report rate and the false report rate are reduced, and the overall performance of network intrusion detection is improved.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to a network intrusion detection method. Background technique [0002] With the development of network technology and network scale, network intrusion is becoming more and more serious, and the security problems such as system damage, information leakage, data damage, and illegal control caused by it have caused a huge threat to the development of the network. In order to ensure network security, various network security technologies emerge as the times require. Among them, network intrusion detection technology has become a research hotspot because of its active defense characteristics, and it is also closely related to the security of network service applications. To distinguish attacks from typical network accesses, machine learning methods are extended to include support vector machines (SVM) and genetic algorithms (GA). [0003] Network intrusion detection is essentia...

AI Technical Summary

Problems solved by technology

The genetic algorithm has a strong global search ability, but the local search ability is poor, and it is difficult to obtain the global

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