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Distance Measurement Methods for Improved Insider Threat Detection.
- Source :
- Security & Communication Networks; 1/17/2018, p1-18, 18p
- Publication Year :
- 2018
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- ACCESS control
DATA security
MACHINE learning
COMPUTER security vulnerabilities
Subjects
Details
- Language :
- English
- ISSN :
- 19390114
- Database :
- Complementary Index
- Journal :
- Security & Communication Networks
- Publication Type :
- Academic Journal
- Accession number :
- 127380016
- Full Text :
- https://doi.org/10.1155/2018/5906368