@inbook{126a2ca823fb4c77880e073da901643e,
title = "Semantic analysis for monitoring insider threats",
abstract = "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.",
author = "Svetlana Symonenko and Liddy, {Elizabeth D.} and Ozgur Yilmazel and {Del Zoppo}, Robert and Eric Brown and Matt Downey",
year = "2004",
doi = "10.1007/978-3-540-25952-7_40",
language = "English (US)",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "492--500",
editor = "Hsinchun Chen and Zeng, {Daniel D.} and Reagan Moore and John Leavitt",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
}