Title: Threat modelling on nuclear and radioactive materials based on intelligent approach

Authors: Altab Hossain; A.Z.M. Salahuddin; M.S. Akbar

Addresses: Department of Nuclear Science and Engineering, Military Institute of Science and Technology, Dhaka 1216, Bangladesh ' Department of Nuclear Science and Engineering, Military Institute of Science and Technology, Dhaka 1216, Bangladesh ' Bangladesh Atomic Energy Commission, Dhaka, Bangladesh

Abstract: Threat modelling and assessments are the processes of gathering, organising and analysing existing or potential threats and deemed to have the capabilities to commit a malicious act. Potential adversaries who may attempt unauthorised removal of nuclear materials (NM) or other radioactive materials (RM) for which a physical protection system (PPS) is designed, and therefore must be assessed and prevented. In case of an undesired condition, the authorities have to carry out analytic activities to detect risky circumstances. Hence, in spite of the various methods for threat modelling, it is essential to systematically analyse these threats. Therefore, in this paper, a threat modelling technique by using fuzzy logic based intelligent approach is designed. The technique involves linking the relationship between input parameters of capability, intent, material and vulnerability and output parameter of threat level for nuclear and radioactive materials and their adaptation for the early forecast of irregular behaviour. For inputs overall capabilities 70%, overall likelihood 60%, and impact 60%, the output threat level is estimated as 76.5% for the domestic group deploying an RDD at an annual celebration. Results obtained from the study show the good performance of the developed model as compared to results considering single fuzzy inference system (SFIS).

Keywords: nuclear materials; physical protection system; threat modelling; fuzzy logic.

DOI: 10.1504/IJNEST.2018.092596

International Journal of Nuclear Energy Science and Technology, 2018 Vol.12 No.1, pp.19 - 31

Received: 03 Mar 2017
Accepted: 26 Jan 2018
Published online: 25 Jun 2018 *

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