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Feature analysis on the containment time for cyber security incidents

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings of the 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)
PublisherIEEE
Pages262-269
Number of pages8
ISBN (Electronic)978-1-5386-5218-3, 978-1-5386-5216-9
ISBN (Print)978-1-5386-5219-0
DOIs
Publication statusPublished - 5 Nov 2018
Event2018 International Conference on Wavelet Analysis and Pattern Recognition - Chengdu, China
Duration: 15 Jul 201818 Jul 2018

Publication series

NameIEEE ICWAPR Proceedings Series
PublisherIEEE
ISSN (Print)2158-5695
ISSN (Electronic)2158-5709

Conference

Conference2018 International Conference on Wavelet Analysis and Pattern Recognition
Abbreviated titleICWAPR 2018
CountryChina
CityChengdu
Period15/07/1818/07/18

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    Accepted author manuscript (Post-print), 325 KB, PDF document

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