Title :
Anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework
Author :
Tsang, Chi-Ho ; Kwong, Sam ; Wang, Hanli
Author_Institution :
Dept. of Comput. Sci., Hong Kong City Univ., Kowloon, China
Abstract :
In this paper, we present a multi-objective genetic fuzzy system for anomaly intrusion detection. The proposed system extracts accurate and interpret able fuzzy rule-based knowledge from network data using an agent-based evolutionary computation framework. The experimental results on KDD-Cup99 intrusion detection benchmark data demonstrate that our system can achieve high detection rate for intrusion attacks and low false positive rate for normal network traffic.
Keywords :
fuzzy systems; genetic algorithms; knowledge based systems; multi-agent systems; security of data; agent-based evolutionary computation; anomaly intrusion detection; fuzzy rule-based knowledge; multiobjective genetic fuzzy system; Biological cells; Computer science; Data mining; Distribution strategy; Evolutionary computation; Fuzzy sets; Fuzzy systems; Gaussian processes; Genetics; Intrusion detection;
Conference_Titel :
Data Mining, Fifth IEEE International Conference on
Print_ISBN :
0-7695-2278-5
DOI :
10.1109/ICDM.2005.26
