An evaluation of clustering technique over intrusion detection system
G. V. Nadiammai, Mrs. S. Hemalatha · 2012
Data mining has been popularly recognized as an important way to mine useful information from large volumes of data that are noisy, fuzzy & random. Intrusion detection has become an efficient tool against network attack because they allow network administrator to detect vulnerability. Existing IDS techniques includes high false positive and false negative rate. Data mining using IDS reduces the number of false alarm rate. So, here some of the clustering algorithms like k means, hierarchical and Fuzzy C Means have been implemented to analyze the detection rate over KDD CUP 99 dataset. Based on evaluation result, FCM outperforms in terms of both accuracy and computational time.