OSINT Analysis using Adaptive Resonance Theory for Conterterrorism Warnings

J.M. Carroll (USA)


Terrorism, OSINT, feature extraction, and neuralnetworks


Open Source Intelligence (OSINT) is an extremely valuable source of data for intelligence analysts in identifying and analyzing potential terrorism warnings and indicators [1]. A key problem is making sense of this large amount of data in time to prevent a catastrophic situation like what occurred on September 11, 2001. These events might been mitigated had U.S. intelligence agencies had better information technology tools for analyzing the situation according to the report of Congress's Joint Inquiry into the events leading up to the Sept. 11 attacks [2]. Improvements to Information and Communications Technologies (ICTs) are necessary to provide the Homeland Security with the proper support for their missions [3].
Artificial Neural Networks (ANNs) have been around for over half a century and their biologically-inspired capability allows functionality similar to the human brain via simulated neurons that can make near-human choices. ANNs are in their genesis with future applications include finance, marketing, medicine and security in data mining [4]. Data mining enables a large amount of data to be sifted and provide avenues to learn or generalize information about that data using feature extraction [5]. Adaptive Resonance Theory (ART) may provide another tool for this analysis.

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