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Intrusion Detection using Fuzzy Data Mining

Journal: International Journal of Computer Science and Mobile Computing - IJCSMC (Vol.3, No. 5)

Publication Date:

Authors : ; ;

Page : 1231-1237

Keywords : IDS; Genetic Algorithm; KDDCUP dataset; rule set; Data Mining; Fuzzy Logic;

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With the rapid expansion of computer networks during the past few years, security has become a crucial issue for modern computer systems. A good way to detect illegitimate use is through monitoring unusual user activity. The solution is an Intrusion Detection System (IDS) which is used to identify attacks and to react by generating an alert or blocking the unwanted data. For IDS, use of genetic algorithm gives huge number of rules which are required for anomaly intrusion detection. These rules will work with high-quality accuracy for detecting the Denial of Service and Probe type of attacks connections and with appreciable accuracy for identifying the U2R and R2L connections. After getting huge rules we apply fuzzy data mining techniques to security system and build a fuzzy data mining based intrusion detection model. These findings from this experiment have given promising results towards applying GA and Fuzzy data mining for Network Intrusion Detection. Performance of the proposed system will be measured using the standard KDD 99 data set.

Last modified: 2014-06-02 19:51:16