Network intrusion detection model based on genetic ant colony algorithm

DOI

10.2991/amcce-15.2015.203How to use a DOI?

Keywords

intrusion characteristics; genetic algorithm; ant colony algorithm; pheromone concentration

Abstract

The traditional network intrusion detection is performed on single-dimensional data feature of invasion, once the intrusion has intrusion feature of abnormally high-dimensional data, which can not achieve a unified detection rules, resulting in decreasing efficiency and accuracy of detection. This paper proposes a network intrusion detection method based on genetic ant colony optimization algorithm. According to genetic algorithm building individual coding, employing fitness function to initialize the population, setting pheromone of ants and establishing global pheromone updating rules by ant colony state transition rules, and then ultimately intrusion detection network is accomplished. Experimental results show that modified algorithm for network intrusion detection can improve the speed of training and testing, with significant advantages on increasing detection rate and reducing fault rate.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jianghao Huang
PY  - 2015/04
DA  - 2015/04
TI  - Network intrusion detection model based on genetic ant colony algorithm
BT  - Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SP  - 650
EP  - 654
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.203
DO  - 10.2991/amcce-15.2015.203
ID  - Huang2015/04
ER  -