Fuzzy Data Mining and Genetic Algorithms Applied to Intrusion Detection

A prototype of intelligent intrusion detection system (IIDS) to demonstrate the effectiveness of data mining techniques that utilize fuzzy logic and genetic algorithms. This system combines both anomaly based intrusion detection using fuzzy data mining techniques and misuse detection using tradition...

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Veröffentlicht in:I-Manager's Journal on Software Engineering Jg. 1; H. 4; S. 23 - 30
Hauptverfasser: Salome, Jacinth, Ravishankar, Ramya
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Nagercoil iManager Publications 01.04.2007
Schlagworte:
Computer information security
Data mining
Deviation
Fuzzy
Fuzzy logic
Fuzzy set theory
Genetic algorithms
Intrusion
ISSN:0973-5151, 2230-7168
Online-Zugang:Volltext
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Zusammenfassung:A prototype of intelligent intrusion detection system (IIDS) to demonstrate the effectiveness of data mining techniques that utilize fuzzy logic and genetic algorithms. This system combines both anomaly based intrusion detection using fuzzy data mining techniques and misuse detection using traditional rule-based expert system techniques. The anomaly-based components are developed using fuzzy data mining techniques. They look for deviations from stored patterns of normal behavior. Genetic algorithms are used to tune the fuzzy membership functions and to select an appropriate set of features. The misuse detection components look for previously described patterns of behavior that are likely to indicate an intrusion. Both network traffic and system audit data are used as inputs for both components.
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ISSN:0973-5151
2230-7168
DOI:10.26634/jse.1.4.731