DocumentCode :
505014
Title :
Intrusion detection system combining misuse detection and anomaly detection using Genetic Network Programming
Author :
Gong, Yunlu ; Mabu, Shingo ; Chen, Ci ; Wang, Yifei ; Hirasawa, Kotaro
Author_Institution :
Grad. Sch. of Inf., Production & Syst., Waseda Univ., Kitakyushu, Japan
fYear :
2009
fDate :
18-21 Aug. 2009
Firstpage :
3463
Lastpage :
3467
Abstract :
In this paper, a class association rule mining approach based on Genetic Network Programming (GNP) for detecting network intrusion combining misuse detection and anomaly detection is proposed. The proposed approach is an extension of the intrusion detection approach using GNP, so it can detect and distinguish normal, known intrusion and unknown intrusion. The simulation result shows that the detection rate is improved compared with traditional intrusion detection approach, and normal, known intrusion and unknown intrusion are distinguished with high accuracy.
Keywords :
genetic algorithms; security of data; anomaly detection; class association rule mining; genetic network programming; known intrusion; misuse detection; network intrusion detection system; normal intrusion; unknown intrusion; Association rules; Computer networks; Data mining; Databases; Economic indicators; Genetics; Intrusion detection; Mathematical programming; Production systems; Protection; Genetic Network Programming; class association rule mining; network intrusion detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
ICCAS-SICE, 2009
Conference_Location :
Fukuoka
Print_ISBN :
978-4-907764-34-0
Electronic_ISBN :
978-4-907764-33-3
Type :
conf
Filename :
5335129
Link To Document :
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