Intrusion detection method based on parallel multi-artificial bee colony algorithm and support vector machine

Abstract

The invention discloses an intrusion detection method based on a parallel multi-artificial bee colony algorithm and a support vector machine. Firstly, an original artificial bee colony algorithm is redesigned, including: design of a nectar source coding scheme, initial design of a population, construction of a fitness evaluation function, a neighborhood search method of a nectar source and calculation of the probability of recruiting observed bees, and in such a way, the problems such as precocious algorithm, poor diversity of solutions, easy fall into local optimum, slow later convergence speed and the like are overcome. Secondly, a multi-bee colony information exchange and collaboration mechanism is designed, and by use of a parallel execution technology, a dual-loop multi-bee colony collaborative optimization model is proposed for synchronous optimization of characteristics and support vector machine model parameters. Then, based on the collaborative optimization model, an intrusion detection method and model based on a parallel multi-artificial bee colony algorithm and a support vector machine are given.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to an intrusion detection method based on a parallel multi-artificial bee colony algorithm and a support vector machine. Background technique [0002] Network intrusion detection system (network intrusion detection system, NIDS) is an active defense system that can make up for the shortcomings of traditional passive firewalls. It is an important part of network security. It detects and identifies illegal intrusions by monitoring and analyzing network data packets in real time. The behavior of computer systems has thus become a research hotspot in recent years. [0003] According to different detection methods, intrusion detection technology can be divided into two categories: misuse detection and anomaly detection. Early detection technology research mainly focused on misuse detection. The classic detection method is pattern matching, which detects intrusion behavior by ob...

AI Technical Summary

Problems solved by technology

Bae et al. applied the ABC algorithm to intrusion detection for the first time, and then Rufai et al. used the improved bee colony algorithm for feature selection and SVM classification to propose a new intrusion detection method, which achieved better results than other detection methods. However, the interdependence of the two optimization processes of SVM parameters and feature selection is also ignored, and there is still a lot of room for improvement in detection performance.

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