Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
A clustering-based method for unsupervised intrusion detections
Pattern Recognition Letters
A hybrid machine learning approach to network anomaly detection
Information Sciences: an International Journal
Extending fuzzy and probabilistic clustering to very large data sets
Computational Statistics & Data Analysis
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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.