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Network Intrusion Detection Using Class Association Rule Mining Based on Genetic Network Programming
Author(s) -
Chen Ci,
Mabu Shingo,
Shimada Kaoru,
Hirasawa Kotaro
Publication year - 2010
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.20572
Subject(s) - intrusion detection system , genetic programming , association rule learning , computer science , data mining , genetic network , network security , class (philosophy) , anomaly detection , misuse detection , anomaly based intrusion detection system , genetic algorithm , artificial intelligence , machine learning , computer security , biochemistry , chemistry , gene
Because of the expansion of the Internet in recent years, computer systems are exposed to an increasing number and type of security threats. How to detect network intrusions effectively becomes an important technique. This paper proposes a class association rule mining approach based on genetic network programming (GNP) for detecting network intrusions. This approach can deal with both discrete and continuous attributes in network‐related data. And it can be flexibly applied to both misuse detection and anomaly detection. Experimental results with KDD99Cup and DARPA98 database from MIT Lincoln Laboratory shows that the proposed method provides a competitive high detection rate (DR) compared to other machine learning techniques. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.