Semantic analysis for monitoring insider threats

Svetlana Symonenko, Elizabeth D Liddy, Ozgur Yilmazel, Robert Del Zoppo, Eric Brown, Matt Downey

Research output: Contribution to journalArticle

11 Scopus citations


Malicious insiders' difficult-to-detect activities pose serious threats to the intelligence community (IC) when these activities go undetected. A novel approach that integrates the results of social network analysis, role-based access monitoring, and semantic analysis of insiders' communications as evidence for evaluation by a risk assessor is being tested on an IC simulation. A semantic analysis, by our proven Natural Language Processing (NLP) system, of the insider's text-based communications produces conceptual representations that are clustered and compared on the expected vs. observed scope. The determined risk level produces an input to a risk analysis algorithm that is merged with outputs from the system's social network analysis and role-based monitoring modules.

Original languageEnglish (US)
Pages (from-to)492-500
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - 2004


ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

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