Generative Artificial Intelligence and Large Language Models for Computer Networking: Applications, Opportunities, and Challenges
Abstract
Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are introducing new paradigms for interacting with, managing, and automating complex technical systems. In computer networking, these technologies have the potential to transform network configuration, troubleshooting, documentation, monitoring, security analysis, and operational decision-making. This review investigates the emerging applications of generative AI and LLMs in networking environments, including natural-language network configuration, automated troubleshooting, configuration generation, network knowledge management, log analysis, code generation, network security operations, and network automation. The paper examines the potential integration of LLMs with Software-Defined Networking (SDN), network controllers, telemetry systems, and network digital twins. Unlike conventional ML models, LLM-based systems introduce additional challenges related to hallucination, reliability, security, grounding, explainability, and deterministic behavior. The review therefore evaluates current approaches for retrieval-augmented generation, tool-augmented LLMs, agentic networking systems, and human-in-the-loop network management. Finally, the paper proposes future research directions for building reliable and secure AI-native networking architectures.
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