Survey on Anomaly Detection using Data Mining Techniques

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 variou...

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Bibliographic Details
Published in:Procedia computer science 2015, Vol.60, p.708-713
Main Authors: Agrawal, Shikha, Agrawal, Jitendra
Format: Article
Language:English
Subjects:
Anomaly Detection
Classification
Clustering
Data Mining
Intrusion Detection System
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Summary: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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2015.08.220