Network Intrusion Detection Using Class Association Rule Mining Based on Genetic Network Programming

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 netw...

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Bibliographic Details
Published in:IEEJ transactions on electrical and electronic engineering Vol. 5; no. 5; pp. 553 - 559
Main Authors: Chen, Ci, Mabu, Shingo, Shimada, Kaoru, Hirasawa, Kotaro
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.09.2010
Wiley
Subjects:
Applied sciences
class association rule mining
Computer systems
Electrical engineering. Electrical power engineering
Electrical power engineering
Electronics
Exact sciences and technology
genetic network programming
Hardware
network intrusion detection
Operation. Load control. Reliability
Power networks and lines
genetic network programming
Evolutionary algorithm
Expert system
class association rule mining
Power system economics
Programming
Experimental study
Learning
network intrusion detection
Genetic algorithm
Database
Power markets
Internet
Open market
Computer system
Safety
Comparative study
ISSN:1931-4973, 1931-4981
Online Access:Get full text
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Summary: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.
Bibliography:ark:/67375/WNG-K93XVXWV-M
istex:A8888F8FCC83B66A8FBD84509AA369AF3F7CAF4A
ArticleID:TEE20572
ISSN:1931-4973
1931-4981
DOI:10.1002/tee.20572