Abstract :

The Rapid Expansion Of Digital Technologies And Interconnected Networks Has Significantly Increased The Risk Of Cyber Threats Across Various Sectors. Traditional Security Systems Often Fail To Detect Emerging Attacks Due To Their Dependence On Predefined Rules And Static Signatures. This Research Presents An Intelligent Cyber Threat Prediction Framework That Leverages Supervised Learning Principles To Identify Malicious Patterns And Predict Potential Attacks Before They Occur. The Proposed Approach Analyzes Historical Security Data And Network Behaviors To Uncover Hidden Patterns Associated With Cyber Risks. By Providing Early Warnings And Improved Situational Awareness, The System Enhances Decision-making In Cybersecurity Operations And Strengthens The Overall Resilience Of Digital Infrastructures. The Results Demonstrate That Predictive Modeling Can Play A Crucial Role In Reducing Vulnerabilities And Ensuring Proactive Threat Management In Modern Network Environments. Keywords: Cybersecurity, Cyber Threat Prediction, Supervised Learning, Machine Learning, Threat Detection, Network Security, Intrusion Detection, Predictive Modeling, Data Analysis, Cyber Risk Assessment

Published:

24-12-2025

Issue:

Vol. 25 No. 12 (2025)

Page Nos:

450-455

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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