Distance Measurement Methods for Improved Insider Threat Detection. (17th January 2018)

• Record Type: Journal Article
• Authors: Lo, Owen; Buchanan, William J.; Griffiths, Paul; Macfarlane, Richard
• Other Names: Pelosi Gerardo Academic Editor.
• Is Part Of: Security and communication networks. Volume 2018(2018)
• Publication Date: 2018-01-17
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2018/5906368
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : 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.