Intent-Based Networking with AI: Towards Fully Autonomous Network Operations

This research explored the integration of Artificial Intelligence (AI) into Intent-Based Networking (IBN) systems with the goal of enabling fully autonomous network operations. Utilizing a qualitative research design, the study involved expert interviews and system behavior observations within simulated environments. The AI-driven IBN prototype was developed with components for natural language intent recognition, policy translation, and autonomous fault management. The findings indicated high accuracy in interpreting user intents (93.7%), efficient policy deployment, and significant reductions in both configuration and recovery times compared to traditional Software-Defined Networking (SDN) systems. Experts validated the system's operational advantages while also noting challenges in handling ambiguous inputs and adapting to diverse network configurations. Overall, the research highlighted the feasibility and benefits of AI-enhanced IBN while recommending further real-world testing and security considerations to achieve truly autonomous network infrastructures.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-29
DOI
https://doi.org/10.5281/zenodo.15347801
Primary Topic
Software-Defined Networks and 5G
Type
article
Field-Weighted Citation Impact
0.00
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article

Intent-Based Networking with AI: Towards Fully Autonomous Network Operations

Zenodo (CERN European Organization for Nuclear Research)
Software-Defined Networks and 5G
article

Intent-Based Networking with AI: Towards Fully Autonomous Network Operations

article en

Abstract

This research explored the integration of Artificial Intelligence (AI) into Intent-Based Networking (IBN) systems with the goal of enabling fully autonomous network operations. Utilizing a qualitative research design, the study involved expert interviews and system behavior observations within simulated environments. The AI-driven IBN prototype was developed with components for natural language intent recognition, policy translation, and autonomous fault management. The findings indicated high accuracy in interpreting user intents (93.7%), efficient policy deployment, and significant reductions in both configuration and recovery times compared to traditional Software-Defined Networking (SDN) systems. Experts validated the system's operational advantages while also noting challenges in handling ambiguous inputs and adapting to diverse network configurations. Overall, the research highlighted the feasibility and benefits of AI-enhanced IBN while recommending further real-world testing and security considerations to achieve truly autonomous network infrastructures.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 52%
Software-Defined Networks and 5G
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Intent-Based Networking with AI: Towards Fully Autonomous Network Operations · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS