Title :
Genetic Algorithm Rule Definition for Denial of Services Network Intrusion Detection
Author :
Wang, Yong ; Gu, Dawu ; Tian, XiuXia ; Li, Jing
Author_Institution :
Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., Shanghai, China
Abstract :
Many previous genetic algorithm methods can get much better example results in KDD cup 99 dataset. In the real intrusion detection system, the software needs update the rule even everyday. In this paper, we expand previous work and present a fitness function, propose an efficient rule generator for denial of services of network intrusion detection. We use more chromosomes with relevant features and more rule generator. As such, the rules generated by our algorithm are suitable to continuously changing misuse detection. In order to verify our approach, we tested our proposal with KDD Cup99 dataset, The experimental results show that the proposed approach is an efficient way in network intrusion detection.
Keywords :
data mining; genetic algorithms; knowledge based systems; security of data; denial of service; fitness function; genetic algorithm rule definition; knowledge discovery; network intrusion detection; Biological cells; Clustering algorithms; Computational intelligence; Computer crime; Computer science; Genetic algorithms; Intrusion detection; Software systems; Support vector machine classification; Support vector machines; denial of services; genetic algorithm; network intrusion detection; rule definition;
Conference_Titel :
Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3645-3
DOI :
10.1109/CINC.2009.106