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
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