A semi-stochastic algorithm approach to generating CBRNE terrorist attack plot scenarios

Abstract

ABSTRACT There are many different types of terrorist attacks, but some types of terrorist attacks are infrequent. One example of an infrequent attack type is one that employs chemical, biological, and radiological weapons. There are also types of attacks that have never happened, such as a nuclear terrorist attack. Given the lack of historical incidents, how can counterterrorism researchers, policymakers, and practitioners utilise a data-driven approach to analyse these types of attacks? To address this question, we introduce the Automated Adversary Template Generation (AATG) process to generate synthetic terrorist incidents that are realistic. The AATG process uses semi-stochastic sampling to simulate terrorist adversary behaviours and activities and produce thousands of CBRNE terrorist attack scenarios. Using these scenarios, individuals researching and working in counterterrorism can analyse possible on-the-horizon Weapons of Mass Destruction/Terror (WMD/WMT) attacks and explore these threat spaces with greater fidelity. In particular, this novel methodology is very useful for red teaming purposes conducted by researchers or counterterrorism practitioners.