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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.

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Intrusion detection system aims at analyzing the severity of network in terms of attack or normal one. Due to the advancement in computer field, there are numerous number of threat exploits attack over huge network. Attack rate increases gradually as detection rate increase. The main goal of using data mining within intrusion detection is to reduce the false alarm rate and to improve the detection rate too. Machine learning algorithms accomplishes to solve the detection problem. In this study, first we analyzed the statistical based anomaly methods such as ALAD, LEARAD and PHAD. Then a new approach is proposed for hybrid intrusion detection. Secondly, the advantage of both supervised and unsupervised has been used to develop a semi-supervised method. Our experimental method is done with the help of KDD Cup 99 dataset. The proposed hybrid IDS detects 149 attacks (nearly 83%) out of 180 attacks by training in one week attack free data. Finally, the proposed semi-supervised approach shows 98.88% accuracy and false alarm rate of 0.5533% after training on 2500 data instances.

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