Using feature analysis to guide risk calculations of cyber incidents

Benjamin Aziz, Alaa Mohasseb

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

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Abstract

The prediction of incident features, for example through the use of text analysis and mining techniques, is one method by which the risk underlying Cyber security incidents can be managed and contained. In this paper, we define risk as the product of the probability of misjudging incident features and the impact such misjudgment could have on incident responses. We apply our idea to a simple case study involving a dataset of Cyber intrusion incidents in South Korean enterprises. We investigate a few problems. First, the prediction of response actions to future incidents involving malware and second, the utilisation of the knowledge of the response actions in guiding analysis to determine the type of malware or the name of the malicious code.
Original languageEnglish
Title of host publicationProceedings of the 18th International Conference on Web Information Systems and Technologies (WEBIST)
EditorsStefan Decker, Francisco Domínguez Mayo , Massimo Marchiori, Joaquim Filipe
PublisherSciTePress
Pages190-195
Number of pages6
Volume1
ISBN (Print)9789897586132
DOIs
Publication statusPublished - 4 Nov 2022
Event18th International Conference on Web Information Systems and Technologies - Valletta, Malta
Duration: 25 Oct 202227 Oct 2022
https://webist.scitevents.org/

Publication series

NameProceedings of the SciTePress International Conference on Web Information Systems and Technologies
PublisherSciTePress
ISSN (Electronic)2184-3252

Conference

Conference18th International Conference on Web Information Systems and Technologies
Abbreviated title(WEBIST 2022)
Country/TerritoryMalta
CityValletta
Period25/10/2227/10/22
Internet address

Keywords

  • cyber security
  • machine learning
  • text mining
  • datasets
  • risk analysis

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