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. First, the original artificial bee colony algorithm is redesigned, including: the design of the honey source coding scheme, the initialization design of the population, and the fitness The construction of the evaluation function, the neighborhood search method of nectar sources and the calculation of the probability of recruiting observation bees overcome the problems of premature algorithm, poor solution diversity, easy to fall into local optimum, and slow convergence speed in the later stage. Secondly, the information exchange and cooperation mechanism among multi-bee colonies is designed, and a double-ring multi-bee colony parallel collaborative optimization model is given by using parallel execution technology, which is used for synchronous optimization of features and support vector machine model parameters. Then, based on the collaborative optimization model, an intrusion detection method and model based on parallel multi-artificial bee colony algorithm and support vector machine is 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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