基于 Stacking 策略的集成 BN 网络目标威胁评估

The existed threat assessment based on Bayesian networks adopts a naive structure determined by expert experience, and its inference evaluation results have poor accuracy. Thus, a Stacking strategy based ensemble Bayesian network (EBN) is proposed that integrates expert experience and data observation. Firstly, scoring optimization algorithms in different search spaces are used to obtain the data observation model set and perform model averaging. Then, expert empirical naive models are used to prune the average network to form a set of threat constraints. Finally, based on dynamic programming, the set is used to limit the expansion of the node order graph, with the aim of obtaining the global optimal threat assessment network. In combat scenarios, the EBN model has a 10% higher inference accuracy for single target threat probability than the naive Bayesian model, and the Spearman coefficient distribution is also better than the naive model in multi target threat ranking tasks.