Journal
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 19TH ANNUAL CONFERENCE, KES-2015
Volume 60, Issue -, Pages 708-713Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2015.08.220
Keywords
Anomaly Detection; Clustering; Classification; Data Mining; Intrusion Detection System
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In the present world huge amounts of data are stored and transferred from one location to another. The data when transferred or stored is primed exposed to 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 to make it less vulnerable. Anomaly detection uses these data mining techniques to detect the surprising behaviour hidden within data increasing the chances of being intruded or attacked. Various hybrid approaches have also been made in order to detect known and unknown attacks more accurately. This paper reviews various data mining techniques for anomaly detection to provide better understanding among the existing techniques that may help interested researchers to work future in this direction. (C) 2015 The Authors. Published by Elsevier B.V.
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