A big data analytics based approach to anomaly detection

Abdul Razaq; Huaglory Tianfield; Peter Barrie

doi:10.1145/3006299.3006317

A big data analytics based approach to anomaly detection

Abdul Razaq, Huaglory Tianfield, Peter Barrie

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

We present a novel Cyber Security analytics framework. We demonstrate a comprehensive cyber security monitoring system to construct cyber security correlated events with feature selection to anticipate behaviour based on various sensors.

Original language	English
Title of host publication	Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
Publisher	ACM
Pages	187-193
Number of pages	7
ISBN (Print)	9781450346177
DOIs	https://doi.org/10.1145/3006299.3006317
Publication status	Published - Dec 2016

Keywords

event correlation
process auditing
IDS/IPS
SIEM
advanced persistent threats
security analytics

Documents and Links

10.1145/3006299.3006317

Razaq, A. et al (2016) A big data analytics based approach to anomaly detection
© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies in December 2016. The final publisher version of the article is available at: http://doi.acm.org/10.1145/3006299.3006317
Accepted author manuscript, 356 KB

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title = "A big data analytics based approach to anomaly detection",

abstract = "We present a novel Cyber Security analytics framework. We demonstrate a comprehensive cyber security monitoring system to construct cyber security correlated events with feature selection to anticipate behaviour based on various sensors.",

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KW - advanced persistent threats

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