Abstract
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.
Highlights
Terrorist attack (TA) has posed a broad threat to the peace, security and stability of the international community
In an attempt to assess the harmfulness of TA consequences more accurately and objectively, based on millions of TA events recorded by Global Terrorism Database (GTD), we first use Random Forest (RF) to select the effective influencing factors of TA, establish an evaluation indicator system, calculate the weight of indicators based on causal Information Flow (IF) and build a new weighted Bayesian Network (BN) through structure learning and parameter learning
In the era of artificial intelligence, our research focuses on the application of BN in the consequence assessment of terrorist attack
Summary
Terrorist attack (TA) has posed a broad threat to the peace, security and stability of the international community. We propose a new causalitybased weight calculation method based on Information Flow (IF), an emerging causal analysis method put forward by Liang [18], and build an improved weighted BN for TA consequence assessment For another problem, we use another ML algorithm, Random Forest (RF) [19], to rank factors according to importance measure to screen the effective influencing factors of TA consequence, which is driven by objective data and avoids subjective randomness. In an attempt to assess the harmfulness of TA consequences more accurately and objectively, based on millions of TA events recorded by Global Terrorism Database (GTD), we first use RF to select the effective influencing factors of TA, establish an evaluation indicator system, calculate the weight of indicators based on causal IF and build a new weighted BN through structure learning and parameter learning.
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