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A New Approach of Intrusion Detection System using Clustering, Classification and Decision Table

Intrusion Detection System (IDS) plays an important role to achieve higher security in finding or detecting malicious activities over network based as well as host based system for a couple of years. There are different types of intrusion detection system and anomaly intrusion detection is one of them. Currently this is often associated with high false alarm with moderate accuracy and detection rates when it’s unable to detect all types of attacks correctly. To overcome this problem, we propose a hybrid approach through a combination of three data mining techniques. These techniques are K-Means clustering, Naïve Bayes classification and Decision Table Majority (rule based) approaches. The proposed hybrid approach clustering all data into the corresponding group before applying a classifier for classification purpose after completing classification resultant data passing to Decision Table Majority (rule based) approaches for final evolution. An experiment is carried out to evaluate the performance of the proposed hybrid approach using KDD Cup ’99 dataset. Result shows that the proposed approach performed better in term of accuracy, detection rate with reasonable false alarm rate and reasonable prediction time.