A big data analytics based approach to anomaly detection

    Research output: Chapter in Book/Report/Conference proceedingConference 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

    Fingerprint

    Feature extraction
    Monitoring
    Sensors
    Big data

    Keywords

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

    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
    Razaq, Abdul ; Tianfield, Huaglory ; Barrie, Peter. / A big data analytics based approach to anomaly detection. Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. ACM, 2016. pp. 187-193
    @inproceedings{bcf8743d1d70494bad08f5e654f82db2,
    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.",
    keywords = "event correlation, process auditing, IDS/IPS, SIEM, advanced persistent threats, security analytics",
    author = "Abdul Razaq and Huaglory Tianfield and Peter Barrie",
    note = "Requested first pub date and AAM 3-3-17 Acceptance email in SAN AAM provided 16-3-17; no embargo required.",
    year = "2016",
    month = "12",
    doi = "10.1145/3006299.3006317",
    language = "English",
    isbn = "9781450346177",
    pages = "187--193",
    booktitle = "Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies",
    publisher = "ACM",

    }

    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. ACM, pp. 187-193. https://doi.org/10.1145/3006299.3006317

    A big data analytics based approach to anomaly detection. / Razaq, Abdul; Tianfield, Huaglory; Barrie, Peter.

    Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. ACM, 2016. p. 187-193.

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

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    AU - Barrie, Peter

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    N2 - 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.

    AB - 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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    DO - 10.1145/3006299.3006317

    M3 - Conference contribution

    SN - 9781450346177

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    BT - Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

    PB - ACM

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    Razaq A, Tianfield H, Barrie P. 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. ACM. 2016. p. 187-193 https://doi.org/10.1145/3006299.3006317