Artificial Intelligence and Big Data Framework for Cybersecurity Resilience in Distributed Networks

The rapid expansion of distributed computer networks, driven by cloud computing, IoT ecosystems, edge computing, and software-defined infrastructures, has increased cybersecurity complexity. The growing volume, velocity, and variety of network data challenge traditional security mechanisms that focus primarily on threat detection, often neglecting system resilience, adaptive response, and recovery. This study develops a resilience-oriented intelligent big data analytics framework integrating Artificial Intelegence (AI), big data processing, and distributed cybersecurity monitoring to strengthen resilience in modern digital environments. A qualitative approach was employed through systematic literature review, conceptual modeling, thematic synthesis, and comparative analysis of existing architectures. The framework consists of four interconnected layers: data acquisition and aggregation, big data processing, intelligent analytics, and adaptive response and recovery. It supports continuous monitoring, anomaly detection, threat prediction, automated mitigation, and recovery orchestration. Comparative analysis indicates that prior studies focus mainly on improving intrusion detection or machine learning techniques, providing limited attention to resilience dimensions such as adaptability, fault tolerance, recovery efficiency, and operational stability. In contrast, the proposed framework integrates intelligent analytics with scalable big data infrastructures and distributed security mechanisms to create a unified resilience-oriented cybersecurity ecosystem. Findings suggest that combining AI-driven analytics, distributed processing, and adaptive security orchestration provides a strategic foundation for enhancing cybersecurity resilience, supporting sustainable digital infrastructure development, and ensuring operational stability in increasingly complex and interconnected network environments.