Recognition of coordinated adversarial behaviors from multi-source information
Georgiy Levchuk, Djuana Lea, Krishna Rao Pattipati · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
To successfully predict the actions of an adversary and develop effective counteractions, knowledge of the enemy's mission and organization are needed. In this paper, we present new models and algorithms to identify behaviors of adversaries based on probabilistic inference of two main signatures of behavior: plans (what the enemy wants to do) and organizations (how the enemy is organized and who is responsible for what). The technology allows extraction, classification, and temporal tracking of behavior signatures using multi-source data, as well as prescribes intelligence collection plans to reduce the ambiguity in current predictions.