Intrusion detection based on K-Means clustering and Naïve Bayes classification

Intrusion Detection System (IDS) plays an effective way to achieve higher security in detecting malicious activities for a couple of years. Anomaly detection is one of intrusion detection system. Current anomaly detection 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 an hybrid learning approach through combination of K-Means clustering and Naïve Bayes classification. The proposed approach will be cluster all data into the corresponding group before applying a classifier for classification purpose. An experiment is carried out to evaluate the performance of the proposed approach using KDD Cup´99 dataset. Result show that the proposed approach performed better in term of accuracy, detection rate with reasonable false alarm rate.