Evolving a Weighted Bayesian Network for Consequence Assessment of Terrorist Attack

Over the past decade, terrorism risk has become a prominent consideration in protecting the well-being of individuals and organizations. Consequence assessment of terrorist attack has become a research hotspot in security science. Aiming at the multi-source, interactional and uncertain factors in terrorism events, we introduce Information Flow (IF) to propose an improved weighted Bayesian Network (BN) with causality-based weights. Based on the weighted BN and Rand Forest (RF), we design a data-driven and expert knowledge-based consequence assessment model of terrorist attack. Firstly, RF is applied to filter effective evaluation indicators objectively. Then, IF is adopted to calculate weights of indicators and the weighted BN is built by structure learning and parameter learning. Finally, assessment experiments are conducted with terrorist attack events recorded in Global Terrorism Database. The results show that our proposed model overcomes the shortcomings of traditional quantitative risk assessment methods in verifiability and flexibility, and can reliably achieve quantitative assessment of terrorist attack risk under multi-source and uncertain information conditions.