Intrusion detection system using data mining a review
Varsha Singh, Shubha Puthran · 2016
Everyday huge amount of information are transferred from one network to another, the information may be exposed to attacks. The information and information system should be protected from unauthorized users. To provide and maintain the Confidentiality and Integrity of the information is a very tedious job so Intrusion Detection plays a very important role. Although various methods are used to protect the information, loopholes exist. Data mining methods is used to analyze different attack patterns in the network. Various Classification, Clustering and Classification via Clustering (CvC) algorithms are reviewed. From the review it has been concluded that CvC would be the best suitable for Intrusion detection. In Intrusion Detection field the Cyber Security and Technology Group Contributed significantly by providing KDDcup 99 dataset and to motivate the researchers by eliminating security and privacy concern. NSL_KDD, GureKDD and Kyoto 2006+ dataset is discussed with their advantages and disadvantages.