1. An Intelligent Approach for Nuclear Security Measures on Nuclear Materials: Demands and Needs
Authors: A.Z.M. Salahuddin, Altab Hossain, R. A. Khan, M.S. Akbar, A. S. Mollah
Presented by
Dr. Md. Altab Hossain, CEng (UK), FIEB
Associate Professor & Program Coordinator,
Dept of Nuclear Science and Engineering
Military Institute of Science and Technology (MIST)
Dhaka, Bangladesh
06 Dec 2016

2. BACKGROUND
Nuclear security practices a no. of preventive acts on NM & RM.
A great number of NM and RM are applied and used worldwide.
Several terrorists may harm society using NM and RM.
Authorities have to carry out analytic and remedial activities.
Risk informed approach is used to identify and assess risks.
Therefore, a risk model can be a good option using FLES.

3. STUDY AREA
Number of researchers used FLES technique in risk assessments.
FLES uses expert appraisals & logical system closer to human. 
A risk assessment model using AI based technique is developed.
Aim is to assess and compare different membership functions.
This approach could be directly applied to real application.

4. RISK ASSESSMENT MODEL Risk model involves few risk factors:
Intent, capability of adversaries, nuclear material (NM) or radioactive material (RM), and vulnerability of target.
Capability: organizational, technical and financial.
Intent of adversaries: ideology and objective.
Possibility of creating a device: material.
Vulnerability of target: type of target and timing of attack.
Assailants break into systems, cause damage that demand risk.

5. METHODOLOGY Fuzzy Inference System (FIS)
Basic FLES comprises of four principal components:
Fuzzification: takes crisp numeric inputs,
Rule base: holds a set of if-then rules,
Inference: creates control actions,
Defuzzification : calculates actual output.
Fig 1. Fundamental
unit of a FES

6. FUZZY INFERENCE SYSTEM (FIS)
FIS is based on fuzzy set theory that calculates level of risk from:
capability, intent, material & vulnerability
Fig.2 Architecture of Fuzzy Logic Based Risk Model

7. FUZZY LOGIC EXPERT SYSTEM (FLES)
Membership Function:
Membership function describes that every single point is mapped to a membership value (Fig. 3 & 4).
For fuzzification, three possible linguistic variables, namely, low (L), medium (M), and high (H) were chosen for inputs.
Five linguistic variables, namely very low (VL), low (L), medium (M), high (H) and very high (VH), were for output.

8. Membership Function: Fig. 3. Membership functions of input
variable “Capability”.
Fig. 4. Membership
functions of output
variable “Risk-
Level”.

9. MATHEMATICAL MODEL USING FES
They may be transformed as likelihood estimates.
Threat sources have different capabilities of adversary, intent, amount of material and type of device, and targeting a system.
A domestic adversary group that has advocated overthrow of any government is considered.
Table I and Table II show the meaning of membership functions:
one input variable capability,
output variable risk level.

10. MATHEMATICAL MODEL USING FES
Fuzzy Sets
Meaning
Low
The adversary has limited resources, expertise, and opportunities to support a successful attack.
Medium
The adversary has moderate resources, expertise, and opportunities to support multiple successful attacks.
High
The adversary has a sophisticated level of expertise, with significant resources and opportunities to support multiple successful coordinated attacks.
TABLE I: ADVERSARY CAPABILITY

11. MATHEMATICAL MODEL USING FES
Fuzzy Sets
Meaning
Very Low
A threat event could have a negligible adverse effect.
Low
A threat event could have a low adverse effect.
Medium
A threat event could have a moderate adverse effect.
High
A threat event could have a severe adverse effect.
Very High
A threat event could have multiple severe adverse effect.
TABLE II: LEVEL OF RISK

12. RESULTS AND DISCUSSIONS
The operation of the fuzzy expert system is shown in Fig. 5.
A certain domestic insurgent group deploying an RDD at an annual celebration.
Fig. 5. Rule Evaluation Viewer

13. RESULTS AND DISCUSSIONS
Risk model can be improved with better accuracy by using MFIS that consists of three FIS.
Risk level is a function of overall capabilities, overall attractiveness of materials, intent, and vulnerability at target as:
Risk = (overall capabilities, overall attractiveness of materials, intent, target)
Overall capabilities = (organizational, technical, financial)
Overall attractiveness of materials = (material type, acquisition, device)

14. RESULTS AND DISCUSSIONS
Fuzzy control surface can serve as a visual depiction over time.
Mesh plots show the relationships between inputs and ouput.
Plots are used to verify the rules and membership functions.
Plot shows that when capability and intent increases, risk will be higher.

15. RESULTS AND DISCUSSIONS
Proposed model can be effectively used for risk assessment.
A good correlation ("High" and "Very High") was found.
Result from FES model is more precise with final risk score.
A terrorist to attack target depends on vulnerabilities, intent to steal information related to NM to cause civilian causalities,
possibility of the terrorist success is very high,
possibility of stealing NM by a terrorist group is very high,
risk of using such NM by terrorists is very high.

16. CONCLUSION An intelligent approach is proposed for nuclear security.
It is the practical modelling in contrast to common risk model.
More precisely and accurately and assist to reduce risk ranking.
Can integrate the opinions of experts in a more accurate way.
In order to deliver intelligent support for the NPP and NM, entire nuclear database are to be considered.
Finally, a state should allocate its resources more effectively for the threats and risks.

17. THANK YOU