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 languageEnglish
    Title of host publicationProceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
    PublisherACM
    Pages187-193
    Number of pages7
    ISBN (Print)9781450346177
    DOIs
    Publication statusPublished - Dec 2016

    Keywords

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

    Documents and Links

    • 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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      • Cite this

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