Fuzzy Soft Set-Based Decision Framework for Human-Centric Cyber Threat Analysis

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Author(s): Ajoy Kanti Das (Tripura University, India), Ebenezer Aquisman Asare (Northeastern University, USA), Samia Daas (University of Batna 2, Algeria), Iqbal M. Batiha (Al Zaytoonah University of Jordan, Jordan), Adam Sandow Saani (Tamale Technical University, Ghana), Nageswara Rao Lakkimsetty (American University of Ras Al Khaimah, UAE)and Abdollah Arasteh (Babol Noshirvani University of Technology, Iran)
Copyright: 2026
Pages: 44
Source title: Cyber Forensic Frameworks for User-Centric Human Threat Intelligence Analysis
Source Author(s)/Editor(s): Seifedine Kadry (Lebanese American University, Lebanon), Mritunjay Rai (Shri Ramswaroop Memorial University, Barabanki, India)and Padmesh Tripathi (Delhi Technical Campus, Greater Noida, India)
DOI: 10.4018/979-8-3373-4898-8.ch002

Keywords: Cyber and Network Security / Information Science Reference / Intelligent Technologies / Knowledge Management and Library Science

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Abstract

People are just as important to cybersecurity as gadgets. Attacks frequently result from human error or manipulation. Common examples include insider misuse, phishing, and weak passwords. These dangers highlight the necessity of researching human behavior in relation to cyberthreats. Users are only marked as "safe" or "unsafe" in older models. However, actual conduct frequently lies in the middle of these two extremes. To deal with such situations, this chapter employs a fuzzy soft set (FSS) structure. Partial risk is represented using fuzzy sets, such as a user who is "moderately risky." Different facets of user behavior and actions are described by soft sets. When combined, FSSs provide a versatile approach to researching Human-Centric Cyber Threats (HCCT). This chapter provides a basic explanation of fuzzy sets, soft sets, and 𝔉." It demonstrates how they aid in the clear analysis of cyber risks. A methodical framework for making decisions is created. A fuzzy soft decision matrix is used to store user behaviors. Low, medium, and high-risk scores are created by combining these numbers. Phishing, insider risks, and sloppy passwords are examples of case studies. Every example demonstrates how FSSs produce more realistic and understandable outcomes. Additionally, an algorithm is presented and its performance is compared in this chapter. For specialists, these are straightforward and transparent, in contrast to machine learning (ML) black boxes. A discussion of the framework's advantages concludes the chapter. It can scale effectively, deal with unpredictability, and change with the times. However, it relies on expert-assigned weights and the quality of the data. In the future, ML for adaptive weights will be added. Real-time cyber monitoring systems can also make advantage of it. It will increase security and transparency when used with explainable AI and blockchain.

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