ARTIFICIAL INTELLIGENCE IN NETWORK ARCHITECTURE: A SYSTEMATIC REVIEW OF INNOVATIONS, IMPLEMENTATIONS, AND FUTURE DIRECTIONS
Keywords:
Artificial Intelligence, Network Architecture, Self-Optimizing Networks, Network Security, Autonomous SystemsAbstract
The integration of Artificial Intelligence (AI) into network architecture represents a transformative shift in how networks are designed, managed, and secured. This article presents a comprehensive analysis of AI's role in modern network infrastructure, examining the implementation of machine learning, deep learning, and natural language processing technologies in network operations. The article investigates the emergence of self-optimizing networks, AI-driven security mechanisms, and automated resource management systems, while critically evaluating their impact on network performance and reliability. Through analysis of industry case studies and current implementations, the article identifies key challenges including data privacy concerns, legacy system integration issues, and workforce adaptation requirements. The article reveals significant advancements in anomaly detection, traffic optimization, and predictive maintenance capabilities through AI integration. The findings suggest that while AI technologies offer substantial improvements in network efficiency and security, successful implementation requires careful consideration of technical, organizational, and human factors. This article contributes to the growing body of knowledge on AI-driven network architecture and provides insights into future developments in autonomous networking systems, particularly in the context of emerging technologies such as 5G and edge computing.
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