In today’s world where everything relies on the networks, the data in transfer may be susceptible to outside attacks. And these attacks are vulnerable because the data is huge in size and critical or may be confidential in nature. Due to this it becomes the prime activity to protect the information and the system processing this huge amount of information from the unauthorized access and theft. And this makes the role of Intrusion detection system very important as this helps in the protection of Confidentiality and maintenance of the integrity and reliability of the information. A number of methods are present and being used to their limits for the protection. Data mining techniques are used for the purpose of pattern extraction and analysis of the attack patterns helps in developing better system for the network. After the review of a number of data mining algorithms for clustering, classifications and classification via clustering (CvC) the conclusion is that CvC algorithm shows the best performance in intrusion detection. In the review datasets like KDDcup 99, NSL_KDD, GureKDD and Kyoto 2006+ is discussed with their performance and results for analysis.

Intrusion, IDS, ID3, C4.5, Classification, Decision Tree, Clustering, Pruning, Classification via Clustering

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