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Author (up) Matt Wolff pdf  openurl
  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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