Feature analysis on the containment time for cyber security incidents

Gulsum Akkuzu; Benjamin Aziz; Han Liu

doi:10.1109/ICWAPR.2018.8521252

Feature analysis on the containment time for cyber security incidents

Gulsum Akkuzu, Benjamin Aziz, Han Liu

School of Computing

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

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Abstract

Data mining techniques have been widely used as a common goal to discover hidden patterns from big data sets, so researchers have been motivated to make use of data in discovering useful information. The main contribution of this paper lies in its identifying relevant features from an open data set to predict the containment time of Cyber incidents. In particular, 13 relevant features were identified and selected to come up with a predictive model. Our results are discussed in the context of the organisation's' information security.

Original language	English
Title of host publication	Proceedings of the 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	262-269
Number of pages	8
ISBN (Electronic)	978-1-5386-5218-3, 978-1-5386-5216-9
ISBN (Print)	978-1-5386-5219-0
DOIs	https://doi.org/10.1109/ICWAPR.2018.8521252
Publication status	Published - 5 Nov 2018
Event	2018 International Conference on Wavelet Analysis and Pattern Recognition - Chengdu, China Duration: 15 Jul 2018 → 18 Jul 2018

Publication series

Name	IEEE ICWAPR Proceedings Series
Publisher	IEEE
ISSN (Print)	2158-5695
ISSN (Electronic)	2158-5709

Conference

Conference	2018 International Conference on Wavelet Analysis and Pattern Recognition
Abbreviated title	ICWAPR 2018
Country/Territory	China
City	Chengdu
Period	15/07/18 → 18/07/18

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10.1109/ICWAPR.2018.8521252

Conference
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Accepted author manuscript (Post-print), 325 KB

Cite this

Akkuzu, G., Aziz, B., & Liu, H. (2018). Feature analysis on the containment time for cyber security incidents. In Proceedings of the 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) (pp. 262-269). Article 18205471 (IEEE ICWAPR Proceedings Series). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICWAPR.2018.8521252

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abstract = "Data mining techniques have been widely used as a common goal to discover hidden patterns from big data sets, so researchers have been motivated to make use of data in discovering useful information. The main contribution of this paper lies in its identifying relevant features from an open data set to predict the containment time of Cyber incidents. In particular, 13 relevant features were identified and selected to come up with a predictive model. Our results are discussed in the context of the organisation's' information security.",

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Akkuzu, G, Aziz, B & Liu, H 2018, Feature analysis on the containment time for cyber security incidents. in Proceedings of the 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)., 18205471, IEEE ICWAPR Proceedings Series, Institute of Electrical and Electronics Engineers Inc., pp. 262-269, 2018 International Conference on Wavelet Analysis and Pattern Recognition, Chengdu, China, 15/07/18. https://doi.org/10.1109/ICWAPR.2018.8521252

Feature analysis on the containment time for cyber security incidents. / Akkuzu, Gulsum; Aziz, Benjamin; Liu, Han.
Proceedings of the 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). Institute of Electrical and Electronics Engineers Inc., 2018. p. 262-269 18205471 (IEEE ICWAPR Proceedings Series).

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

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AB - Data mining techniques have been widely used as a common goal to discover hidden patterns from big data sets, so researchers have been motivated to make use of data in discovering useful information. The main contribution of this paper lies in its identifying relevant features from an open data set to predict the containment time of Cyber incidents. In particular, 13 relevant features were identified and selected to come up with a predictive model. Our results are discussed in the context of the organisation's' information security.

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Feature analysis on the containment time for cyber security incidents

Abstract

Publication series

Conference

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