期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2017
卷号：6
期号：8
页码：17195
DOI：10.15680/IJIRSET.2017.0608216
出版社：S&S Publications
摘要：In Information Security, intrusion detection is the act of detecting actions that attempt to compromisethe security goal. One of the primary challenge to intrusion detection is the problem of misjudgement , misdetectionand lack of real time response to the attack. Although various techniques or applications are available to protect data,loopholes exist. Thus to analyze data and to determine various kind of attack data mining techniques have emerged tomake it less vulnerable. Anomaly detection uses these datamining techniques to detect the surprising behaviour hiddenwithin data increasing the chances of being intruded or attacked. Various data mining techniques as clustering,classification and association rule discovery are being used for intrusion detection. The proposed technique combinesdata mining approaches like K Means clustering algorithm and RBF kernel function of Support Vector Machine as aclassification modules. The main intention of proposed technique is to decrease the number of attributes associatedwith each data point. So, the future technique can perform better in terms of Detection Rate and Accuracy when appliedto KDDCUP’99 Data Set. This paper reviews various data mining techniques for anomaly detection to provide betterunderstanding among the existing techniques that may help interested researchers to work future in this direction.
关键词：Data Mining; Anomaly Detection; K- means Clustering; Classification; Intrusion Detection System;KDD data set.
