Intrusion detection: a novel approach that combines boosting genetic fuzzy classifier and data mining techniques

Tansel Ozyer; Reda Alhajj; Ken Barker

doi:10.1117/12.603273

28 March 2005 Intrusion detection: a novel approach that combines boosting genetic fuzzy classifier and data mining techniques

Tansel Ozyer, Reda Alhajj, Ken Barker

Author Affiliations +

Proceedings Volume 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005; (2005) https://doi.org/10.1117/12.603273
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

This paper proposes an intelligent intrusion detection system (IDS) which is an integrated approach that employs fuzziness and two of the well-known data mining techniques: namely classification and association rule mining. By using these two techniques, we adopted the idea of using an iterative rule learning that extracts out rules from the data set. Our final intention is to predict different behaviors in networked computers. To achieve this, we propose to use a fuzzy rule based genetic classifier. Our approach has two main stages. First, fuzzy association rule mining is applied and a large number of candidate rules are generated for each class. Then the rules pass through pre-screening mechanism in order to reduce the fuzzy rule search space. Candidate rules obtained after pre-screening are used in genetic fuzzy classifier to generate rules for the specified classes. Classes are defined as Normal, PRB-probe, DOS-denial of service, U2R-user to root and R2L- remote to local. Second, an iterative rule learning mechanism is employed for each class to find its fuzzy rules required to classify data each time a fuzzy rule is extracted and included in the system. A Boosting mechanism evaluates the weight of each data item in order to help the rule extraction mechanism focus more on data having relatively higher weight. Finally, extracted fuzzy rules having the corresponding weight values are aggregated on class basis to find the vote of each class label for each data item.

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Tansel Ozyer, Reda Alhajj, and Ken Barker "Intrusion detection: a novel approach that combines boosting genetic fuzzy classifier and data mining techniques", Proc. SPIE 5812, Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005, (28 March 2005); https://doi.org/10.1117/12.603273

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KEYWORDS

Fuzzy logic

Genetics

Mining

Computer intrusion detection

Genetic algorithms

Data mining

Databases

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