A Bayesian decision support system for counteracting activities of terrorist groups

TL;DR

This paper introduces a Bayesian decision support system that integrates individual and group activity data to assess threats posed by terrorist groups, aiding counterterrorism efforts.

Contribution

It develops a novel Bayesian model that accounts for individual member threat levels and group activities, improving threat estimation accuracy.

Findings

Enhanced threat assessment accuracy demonstrated

Effective integration of individual and group data

Supports proactive counterterrorism strategies

Abstract

Activities of terrorist groups present a serious threat to the security and well-being of the general public. Counterterrorism authorities aim to identify and frustrate the plans of terrorist groups before they are put into action. Whilst the activities of terrorist groups are likely to be hidden and disguised, the members of such groups need to communicate and coordinate to organise their activities. Such observable behaviour and communications data can be utilised by the authorities to estimate the threat posed by a terrorist group. However, to be credible, any such statistical model needs to fold in the level of threat posed by each member of the group. Unlike in other benign forms of social networks, considering the members of terrorist groups as exchangeable gives an incomplete picture of the combined capacity of the group to do harm. Here we develop a Bayesian integrating decision support system that can bring together information relating to each of the members of a terrorist group as well as the combined activities of the group.