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Unsupervised methods for detecting a malicious insider
Matt Wolff
author
2010
Information Systems for Crisis Response and Management, ISCRAM
Seattle, WA
English
One way a malicious insider can attack a network is by masquerading as a different user. Various algorithms have been proposed in an effort to detect when a user masquerade attack has occurred. In this paper, two unsupervised algorithms are proposed with the intended goal of detecting user masquerade attacks. The effectiveness of these two unsupervised algorithms are then compared against supervised algorithms.
Information systems
Natural language processing systems
Network security
Unsupervised learning
Insider Threat
Malicious insiders
Masquerade attacks
Supervised algorithm
Unsupervised algorithms
Unsupervised method
User masquerades
Algorithms
exported from refbase (http://idl.iscram.org/show.php?record=1097), last updated on Sat, 08 Aug 2015 12:44:42 +0200
text
http://idl.iscram.org/files/wolff/2010/1097_Wolff2010.pdf
MattWolff2010
ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings
ISCRAM 2010
S. French
B
Tomaszewski
editor
7th International ISCRAM Conference on Information Systems for Crisis Response and Management
2010
Information Systems for Crisis Response and Management, ISCRAM
Seattle, WA
conference publication
2411-3387
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