An approach to implement a network intrusion detection system using genetic algorithms

Authors:
M. M. Pillai;Jan H. P. Eloff;H. S. Venter
Affiliations:
Information and Computer Security (ICSA) Research Group, Department of Computer Science, University of Pretoria, Pretoria, 0002, South Africa;Information and Computer Security (ICSA) Research Group, Department of Computer Science, University of Pretoria, Pretoria, 0002, South Africa;Information and Computer Security (ICSA) Research Group, Department of Computer Science, University of Pretoria, Pretoria, 0002, South Africa
Venue:
SAICSIT '04 Proceedings of the 2004 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Year:
2004

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Abstract

As the transmission of data over the internet increases, the need to protect connected systems also increases. Intrusion Detection Systems (IDSs) are the latest technology used for this purpose. Although the field of IDSs is still developing, the systems that do exist are still not complete, in the sense that they are not able to detect all types of intrusions. Some attacks which are detected by various tools available today cannot be detected by other products, depending on the types and methods that they are built on. Using a Genetic Algorithm (GA) is one of the methods that IDSs use to detect intrusions. They incorporate the concept of Darwin's theory and natural selection to detect intrusions. Not much research has been conducted in this area besides the Genetic Algorithm as an Alternative Tool for Security Audit Trails Analysis (GASSATA) tool; there are very few IDSs that are completely developed from using GAs. The focus of this paper is to introduce the application of GA, in order to improve the effectiveness of IDSs.