DB&DL: Threat Classification and Reasoning by Dynamic Bayesian Network, Description Logic, and Intuitionistic Fuzzy Sets

InfoBeyond advocates DB&DL (Threat Classification and Reasoning by Dynamic Bayesian Network, Description Logic, and Intuitionistic Fuzzy Sets) to provide threat classification refinement with advanced contextual reasoning capabilities. It takes all the sensing data, text/messages, and human judgments into account for optimal defense decision-making through the full life cycle of a threat. Advanced contextual reasoning is achieved by leveraging Bayesian theory, dynamic graphs, Description Logic (DL), text mining, and Fuzzy technologies. Dynamic Bayesian Network (DBN) is applied to threat graphs to derive threat classification or degrees of threat confidence. DBN allows probabilistic event reasoning (e.g., cause and consequence probability) while fusing the spatial and temporal dependent features of observations. Influence Diagram (ID) with DL is empowered for sophisticate contextual reasoning, such as the likelihood of a consequence to hold of a threat giving the context, the optimistic and pessimistic outcome of a responding strategy, and the optimal strategy with response to the threat. Further, Multi-attribute Matrix (MM) with Intuitionistic Fuzzy Sets (IFS) performs a risk assessment to sort the degree of threats if multiple threats appear such that defending resources can be optimally allocated. Approved for Public Release | 21-MDA-11013 (19 Nov 21)