Semantic analysis for monitoring insider threats
Year: 2004
Type: article
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.
Authors: Svetlana Symonenko, Elizabeth D. Liddy, Özgür Yılmazel, Robert Del Zoppo, Eric W. Brown, Matt Downey
Institutions: Syracuse University, SRC
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2588509416