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

18 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 language	English
Title of host publication	Proceedings
Subtitle of host publication	3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016
Editors	Ashiq Anjum, Xinghui Zhao
Place of Publication	Los Alamitos, CA
Publisher	Association for Computing Machinery, Inc
Pages	187-193
Number of pages	7
ISBN (Electronic)	9781450346177
DOIs	https://doi.org/10.1145/3006299.3006317
Publication status	Published - 1 Dec 2016
Externally published	Yes
Event	IEEE/ACM 3rd International Conference on Big Data Computing, Applications and Technologies - Tongji University, Shanghai, China Duration: 6 Dec 2016 → 9 Dec 2016 Conference number: 3rd

Conference

Conference	IEEE/ACM 3rd International Conference on Big Data Computing, Applications and Technologies
Abbreviated title	BDCAT 2016
Country/Territory	China
City	Shanghai
Period	6/12/16 → 9/12/16

Keywords

Even correlation
Process auditing
IDS/IPS
SIEM
Advanced persistent threats
Security analytics

Access to Document

10.1145/3006299.3006317

Abdul Razaq
- a.razaqabertay.acuk
Person: Academic

Cite this

@inproceedings{fae692a550f04519ad777c0638b6dd5c,

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.",

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

year = "2016",

month = dec,

day = "1",

doi = "10.1145/3006299.3006317",

language = "English",

pages = "187--193",

editor = "Ashiq Anjum and Xinghui Zhao",

booktitle = "Proceedings",

publisher = "Association for Computing Machinery, Inc",

note = "IEEE/ACM 3rd International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

}

Razaq, A, Tianfield, H & Barrie, P 2016, A big data analytics based approach to anomaly detection. in A Anjum & X Zhao (eds), Proceedings: 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016. Association for Computing Machinery, Inc, Los Alamitos, CA, pp. 187-193, IEEE/ACM 3rd International Conference on Big Data Computing, Applications and Technologies, Shanghai, China, 6/12/16. https://doi.org/10.1145/3006299.3006317

A big data analytics based approach to anomaly detection. / Razaq, Abdul; Tianfield, Huaglory; Barrie, Peter.
Proceedings: 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016. ed. / Ashiq Anjum; Xinghui Zhao. Los Alamitos, CA: Association for Computing Machinery, Inc, 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 - Conference code: 3rd

PY - 2016/12/1

Y1 - 2016/12/1

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.

U2 - 10.1145/3006299.3006317

DO - 10.1145/3006299.3006317

M3 - Conference contribution

AN - SCOPUS:85013176226

SP - 187

EP - 193

BT - Proceedings

A2 - Anjum, Ashiq

A2 - Zhao, Xinghui

PB - Association for Computing Machinery, Inc

CY - Los Alamitos, CA

T2 - IEEE/ACM 3rd International Conference on Big Data Computing, Applications and Technologies

Y2 - 6 December 2016 through 9 December 2016

ER -

A big data analytics based approach to anomaly detection

Abstract

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Keywords

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Abdul Razaq

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