Network intrusion detection using fuzzy class association rule mining based on genetic network programming

Computer systems are exposed to an increasing number and type of security threats due to the expanding of Internet in recent years. How to detect network intrusions effectively becomes an important techniques. This paper presents a novel fuzzy class association rule mining method based on Genetic Ne...

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Veröffentlicht in:2009 IEEE International Conference on Systems, Man and Cybernetics S. 60 - 67
Hauptverfasser: Ci Chen, Mabu, S., Chuan Yue, Shimada, K., Hirasawa, K.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2009
Schlagworte:
Association rules
class association rule mining
Computer security
Data mining
Economic indicators
fuzzy membership function
Genetic algorithms
Genetic Network Programming
Genetic programming
Internet
Intrusion detection
network intrusion detection
Tree graphs
ISBN:9781424427932, 1424427932
ISSN:1062-922X
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Abstract Computer systems are exposed to an increasing number and type of security threats due to the expanding of Internet in recent years. How to detect network intrusions effectively becomes an important techniques. This paper presents a novel fuzzy class association rule mining method based on Genetic Network Programming (GNP) for detecting network intrusions. GNP is an evolutionary optimization techniques, which uses directed graph structures as genes instead of strings (Genetic Algorithm) or trees (Genetic Programming), leading to creating compact programs and implicitly memorizing past action sequences. By combining fuzzy set theory with GNP, the proposed method can deal with the mixed database which contains both discrete and continuous attributes. And it can be flexibly applied to both misuse and anomaly detection in Network Intrusion Detection Problem. Experimental results with KDD99Cup and DAPRA98 databases from MIT Lincoln Laboratory show that the proposed method provides a competitively high detection rate compared with other machine learning techniques.
AbstractList Computer systems are exposed to an increasing number and type of security threats due to the expanding of Internet in recent years. How to detect network intrusions effectively becomes an important techniques. This paper presents a novel fuzzy class association rule mining method based on Genetic Network Programming (GNP) for detecting network intrusions. GNP is an evolutionary optimization techniques, which uses directed graph structures as genes instead of strings (Genetic Algorithm) or trees (Genetic Programming), leading to creating compact programs and implicitly memorizing past action sequences. By combining fuzzy set theory with GNP, the proposed method can deal with the mixed database which contains both discrete and continuous attributes. And it can be flexibly applied to both misuse and anomaly detection in Network Intrusion Detection Problem. Experimental results with KDD99Cup and DAPRA98 databases from MIT Lincoln Laboratory show that the proposed method provides a competitively high detection rate compared with other machine learning techniques.
Author Mabu, S.
Ci Chen
Hirasawa, K.
Chuan Yue
Shimada, K.
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StartPage 60
SubjectTerms Association rules
class association rule mining
Computer security
Data mining
Economic indicators
fuzzy membership function
Genetic algorithms
Genetic Network Programming
Genetic programming
Internet
Intrusion detection
network intrusion detection
Tree graphs
Title Network intrusion detection using fuzzy class association rule mining based on genetic network programming
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