Parallel intrusion detection method and system based on unbalanced data deep belief network

Abstract

The invention discloses a parallel intrusion detection method based on an unbalanced data deep belief network, and the method comprises the steps: reading unbalanced data set data, carrying out the undersampling of the unbalanced data through employing an improved NCL algorithm, reducing the proportion of most types of samples, and enabling the distribution of the data set data to be balanced; optimizing parameters of the deep belief network model on a distributed memory computing platform Spark platform by adopting an improved differential evolution algorithm to obtain optimal model parameters; performing feature extraction on data of a data set, performing intrusion detection classification by adopting a weighted kernel extreme learning machine, training a plurality of weighted kernel extreme learning machines with different structures in parallel through multiple threads to serve as base classifiers, and establishing a multi-classifier intrusion detection model based on adaptive weighted voting to perform parallel intrusion detection. The technical problems that an existing intrusion detection method lacks pertinence for an unbalanced data set and training time is too long can be solved, and the speed of optimizing parameters of the deep belief network model is increased.

Description

technical field [0001] The invention belongs to the technical field of intrusion detection, and more specifically relates to a parallel intrusion detection method and system based on unbalanced data deep belief network. Background technique [0002] With the development of society, people pay more and more attention to the issue of network security. The intrusion detection method is an effective active defense method for network security issues. It judges whether there are abnormal intrusion behaviors in the network by detecting information such as traffic in the network. Compared with the firewall, the security of the intrusion detection method is better, it not only needs less resources, basically does not affect the normal operation of the system, but also can be adjusted dynamically. [0003] The current mainstream intrusion detection methods mainly include: 1. The intrusion detection method based on unbalanced data. Low technical problem, data optimization starts from...

AI Technical Summary

Problems solved by technology

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a parallel intrusion detection method and system based on unbalanced data deep belief network, the purpose of which is to solve the lack of pertinence of existing intrusion detection methods for unbalanced data sets At the same time, the speed of optimizing the parameters of the deep belief network model is improved. Finally, the invented method can effectively improve the detection accuracy and detection speed of intrusion detection.

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