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Survey on Anomaly Detection using Data Mining Techniques

Open access
Agrawal, Shikha, Agrawal, Jitendra·2015·Procedia Computer Science

Abstract

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.

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Metadata

Authors
Agrawal, S., & Agrawal, J.
Venue
Procedia Computer Science
Publisher
Elsevier BV
Published
2015
Volume
60
Pages
708-713
ISSN
1877-0509 (ISSN-L)
OpenAlex
W2613480438
Cited by
595
References
0
FWCI
21.47
Type
conference paper
Language
en
Open access
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CoChat. (2026). Citation check: Survey on Anomaly Detection using Data Mining Techniques. https://app.cochat.ai/papers/10.1016/j.procs.2015.08.220
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Agrawal, S., & Agrawal, J. (2015). Survey on Anomaly Detection using Data Mining Techniques. Procedia Computer Science. https://doi.org/10.1016/j.procs.2015.08.220

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.