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

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.