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

12 Citations (Scopus)

295 Downloads (Pure)

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	Association for Computing Machinery (ACM)
Pages	187-193
Number of pages	7
ISBN (Print)	9781450346177
DOIs	https://doi.org/10.1145/3006299.3006317
Publication status	Published - 6 Dec 2016

Keywords

Advanced Persistent Threats
Event Correlation
IDS/IPS
Process Auditing
SIEM
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
Author accepted manuscript, 356 KB

Cite this

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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.",

keywords = "Advanced Persistent Threats, Event Correlation, IDS/IPS, Process Auditing, SIEM, Security Analytics",

author = "Abdul Razaq and Huaglory Tianfield and Peter Barrie",

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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. Association for Computing Machinery (ACM), 2016. p. 187-193.

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

TY - GEN

T1 - A big data analytics based approach to anomaly detection

AU - Razaq, Abdul

AU - Tianfield, Huaglory

AU - Barrie, Peter

N1 - Requested first pub date and AAM 3-3-17 Acceptance email in SAN AAM provided 16-3-17; no embargo required.

PY - 2016/12/6

Y1 - 2016/12/6

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.

KW - Advanced Persistent Threats

KW - Event Correlation

KW - IDS/IPS

KW - Process Auditing

KW - SIEM

KW - Security Analytics

U2 - 10.1145/3006299.3006317

DO - 10.1145/3006299.3006317

M3 - Conference contribution

SN - 9781450346177

SP - 187

EP - 193

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

PB - Association for Computing Machinery (ACM)

ER -

A big data analytics based approach to anomaly detection

Abstract

Keywords

Documents and Links

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Cite this