Distance Measurement Methods for Improved Insider Threat Detection
Joint Authors
Lo, Owen
Buchanan, William J.
Griffiths, Paul
Macfarlane, Richard
Source
Security and Communication Networks
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-17
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Information Technology and Computer Science
Abstract EN
Insider threats are a considerable problem within cyber security and it is often difficult to detect these threats using signature detection.
Increasing machine learning can provide a solution, but these methods often fail to take into account changes of behaviour of users.
This work builds on a published method of detecting insider threats and applies Hidden Markov method on a CERT data set (CERT r4.2) and analyses a number of distance vector methods (Damerau–Levenshtein Distance, Cosine Distance, and Jaccard Distance) in order to detect changes of behaviour, which are shown to have success in determining different insider threats.
American Psychological Association (APA)
Lo, Owen& Buchanan, William J.& Griffiths, Paul& Macfarlane, Richard. 2018. Distance Measurement Methods for Improved Insider Threat Detection. Security and Communication Networks،Vol. 2018, no. 2018, pp.1-18.
https://search.emarefa.net/detail/BIM-1214245
Modern Language Association (MLA)
Lo, Owen…[et al.]. Distance Measurement Methods for Improved Insider Threat Detection. Security and Communication Networks No. 2018 (2018), pp.1-18.
https://search.emarefa.net/detail/BIM-1214245
American Medical Association (AMA)
Lo, Owen& Buchanan, William J.& Griffiths, Paul& Macfarlane, Richard. Distance Measurement Methods for Improved Insider Threat Detection. Security and Communication Networks. 2018. Vol. 2018, no. 2018, pp.1-18.
https://search.emarefa.net/detail/BIM-1214245
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214245