Feature analysis on the containment time for cyber security incidents

Akkuzu, Gulsum, Aziz, Benjamin and Liu, Han ORCID: https://orcid.org/0000-0002-7731-8258 2018. Feature analysis on the containment time for cyber security incidents. Presented at: International Conference on Machine Learning and Cybernetics (ICMLC 2018), Chengdu, China, 15-18 July 2018. 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, pp. 262-269. 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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	IEEE
ISBN:	978-1-5386-5218-3
Related URLs:	Organisation
Date of First Compliant Deposit:	6 July 2018
Date of Acceptance:	17 May 2018
Last Modified:	25 Oct 2022 13:26
URI:	https://orca.cardiff.ac.uk/id/eprint/119821

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• Feature analysis on the containment time for cyber security incidents. (deposited 06 Jul 2018 09:10)
  • Feature analysis on the containment time for cyber security incidents. (deposited 15 May 2019 10:15) [Currently Displayed]

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