A big data analytics based approach to anomaly detection

Abdul Razaq, Huaglory Tianfield, Peter Barrie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings
Subtitle of host publication3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016
Place of PublicationLos Alamitos, CA
PublisherAssociation for Computing Machinery, Inc
Pages187-193
Number of pages7
ISBN (Electronic)9781450346177
DOIs
Publication statusPublished - 1 Dec 2016
Externally publishedYes
EventIEEE/ACM 3rd International Conference on Big Data Computing, Applications and Technologies - Tongji University, Shanghai, China
Duration: 6 Dec 20169 Dec 2016
Conference number: 3rd

Conference

ConferenceIEEE/ACM 3rd International Conference on Big Data Computing, Applications and Technologies
Abbreviated titleBDCAT 2016
CountryChina
CityShanghai
Period6/12/169/12/16

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    Razaq, A., Tianfield, H., & Barrie, P. (2016). A big data analytics based approach to anomaly detection. In Proceedings: 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016 (pp. 187-193). Association for Computing Machinery, Inc. https://doi.org/10.1145/3006299.3006317