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Feature analysis on the containment time for cyber security incidents

Akkuzu, Gulsum, Aziz, Benjamin and Liu, Han ORCID: 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.

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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: Completion
Status: In Press
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
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Date of First Compliant Deposit: 6 July 2018
Last Modified: 23 Oct 2022 14:10

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