Open Access

ARTICLE

Classification of Cyber Threat Detection Techniques for Next-Generation Cyber Defense via Hesitant Bipolar Fuzzy Frank Information

Hafiz Muhammad Waqas¹, Tahir Mahmood^1,2, Walid Emam³, Ubaid ur Rehman⁴, Dragan Pamucar^5,*

1 Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, 44000, Pakistan
2 SK-Research-Oxford Business College, Oxford, OX1 2EP, UK
3 Department of Statistics and Operations Research, Faculty of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
4 Department of Mathematics, University of Management and Technology, C-II, Johar Town, Lahore, 54700, Punjab, Pakistan
5 Transport and Logistics Competence Centre, Vilnius Gediminas Technical University, Vilnius, LT-10223, Lithuania

* Corresponding Author: Dragan Pamucar. Email:

Computers, Materials & Continua 2025, 84(3), 4699-4727. https://doi.org/10.32604/cmc.2025.065011

Received 28 February 2025; Accepted 27 April 2025; Issue published 30 July 2025

Abstract

Cyber threat detection is a crucial aspect of contemporary cybersecurity due to the depth and complexity of cyberattacks. It is the identification of malicious activity, unauthorized access, and possible intrusions in networks and systems. Modern detection methods employ artificial intelligence and machine learning to study vast amounts of data, learn patterns, and anticipate potential threats. Real-time monitoring and anomaly detection improve the capacity to react to changing threats more rapidly. Cyber threat detection systems aim to reduce false positives and provide complete coverage against the broadest possible attacks. This research advocates for proactive measures and adaptive technologies in defending digital environments. Improvements in detection ability by organizations will assist in safeguarding assets and integrity in operations in this increasingly digital world. This paper draws on the categorization of cyber threat detection methods using hesitant bipolar fuzzy Frank operators. Categorization is a step that is necessary for systematic comparison and assessment of detection methods so that the most suitable method for particular cybersecurity requirements is chosen. Furthermore, this research manages uncertainty and vagueness that exists in decision-making by applying hesitant bipolar fuzzy logic. The importance of the work lies in how it fortifies cybersecurity architectures with a formal method of discovering optimal detection measures and improving responsiveness, resulting in holistic protection against dynamic threats.

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Keywords

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