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Author
Matt Wolff
Title
Unsupervised methods for detecting a malicious insider
Type
Conference Article
Year
2010
Publication
ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings
Abbreviated Journal
ISCRAM 2010
Volume
Issue
Pages
Keywords
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
Abstract
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.
Address
University of Hawaii, United States
Corporate Author
Thesis
Publisher
Information Systems for Crisis Response and Management, ISCRAM
Place of Publication
Seattle, WA
Editor
S. French, B. Tomaszewski, C. Zobel
Language
English
Summary Language
English
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
2411-3387
ISBN
Medium
Track
Special Session: Information Credibility, Trust, Privacy and Security in Information Systems for Emergency Management
Expedition
Conference
7th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes
Approved
no
Call Number
Serial
1097
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