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.
Symonenko et al prove Natural Language Processing (NLP) system to integrate the results of social network analysis, role-based access monitoring, and semantic analysis of insiders' communications REF .
Semantic analysis for monitoring insider threats
The 2nd NSF/NIJ Symposium on Intelligence and Security Informatics (ISI 2004). Tucson,AZ
Computer Science