INTELLIGENT CYBERSECURITY FRAMEWORK MACHINE LEARNING-DRIVEN DATA PROTECTION AND THREAT INTELLIGENCE INTEGRATION FOR MODERN DIGITAL COMMUNICATIONS

Abstract

The exponential growth of digital communications, driven by cloud computing, IoT, 5G, and edge technologies, has significantly expanded the cyber-security threat landscape. Traditional rule-based defense mechanisms are increasingly inadequate against sophisticated attacks such as advanced persistent threats (APTs), and polymorphic malware. In response, machine learning (ML) and artificial intelligence (AI) have emerged as transformative tools for enhancing cyber-security frameworks, enabling adaptive data protection and real-time threat intelligence integration. Research Objective:This study aims to design and evaluate an intelligent cybersecurity framework that leverages machine learning techniques to improve data protection and integrate threat intelligence for securing modern digital communications. Research Methods:A review-based methodology was employed, synthesizing literature published between 2012 and 2024. Sources included peer-reviewed journals, government publications (e.g., NIST, CISA), and industry reports. The study applied systematic search strategies using academic databases and threat intelligence platforms. Conclusion:Findings indicate that ML-driven cyber-security frameworks outperform traditional models in threat detection precision, response latency, and adaptability. Integration of real-time threat intelligence significantly enhances situational awareness and incident response capabilities.