A Personalized and Practical Method for Analyzing the Risk of Chemical Terrorist Attacks

Abstract

The chemical terrorist attack is a type of unconventional terrorism that threatens the safety of cities. This kind of terrorist attack is highly concealed and difficult to be detected. Once the attack is successful, the consequences will be severe and the scope of impact will be enormous. Therefore, public security and emergency departments need to perform risk analysis and dynamic knowledge update to reduce risk or mitigate the effects of accidents. In order to quickly and effectively analyze the risk of chemical terrorist attacks, this article proposed a hybrid approach (B-R model) to analyze the risk of chemical terrorist attacks. First, a modular and customizable Bayesian network (BN) model library was built, which can satisfy users to select multi-dimensional risk factors. Based on the personalized BN, a risk knowledge graph (RKG) is constructed with multi-source data to realize the combination of risk analysis and knowledge acquisition. Then the threat degree of terrorist organizations, the strength of defensive forces, and the risk value of targets is calculated and displayed. The BN-RKG method provides data and theoretical support for defenders' resource allocation and emergency decision-making. Finally, a case study was conducted for a hypothetical scenario analysis. The result shows that the hybrid method can help with risk control and have the potential to support practical policymaking.

Keywords

• Chemical terrorist attack
• Bayesian network
• knowledge graph
• risk analysis