A Survey on Intent-Based Networking

Leivadeas, Aris; Falkner, Matthias

doi:10.1109/comst.2022.3215919

Cited by 78 publications

(20 citation statements)

References 115 publications

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“…IBN represents an evolution in the way networks are managed and orchestrated. It is a form of network administration that introduces an extra abstraction layer to automate administrative tasks across a network, often by leveraging artificial intelligence (AI) and machine learning (ML) [1]. The fundamental idea of IBN is to allow network administrators to manage networks in accordance with the "business intent" or the organization's high-level business objectives.…”

Section: A Backgroundmentioning

confidence: 99%

“…Security in Intent-Based Networking: Challenges and Solutions Ijaz Ahmad * , Jere Malinen * , Filippos Christou † , Pawani Porambage * , Andreas Kirstädter † , Jani Suomalainen * I. INTRODUCTION Intent-Based Networking (IBN) is an emerging paradigm for network configuration, which combines concepts from network abstraction, softwarization, automation, and artificial intelligence [1]. It promises user-friendly, cost-efficient, resilient, and secure configuration by presenting network and security requirements as simple intents that govern complex environments, including 6G and multi-domain networks.…”

mentioning

confidence: 99%

“…Existing surveys on IBN, including [1], [2], have studied challenges and opportunities for different application domains, but have not focused on cybersecurity challenges or solutions. We contribute by analyzing security threats and solutions and by surveying existing research efforts for security within IBN.…”

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confidence: 99%

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Security in Intent-Based Networking: Challenges and Solutions

Ahmad,

Malinen,

Christou

et al. 2023

2023 IEEE Conference on Standards for Communications and Networking (CSCN)

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Intent-Based Networking (IBN) aims to automate administrative and management tasks in future communications networks. By leveraging networking concepts such as network abstractions and data-plane programmability and using artificial intelligence (AI), IBN improves the overall efficiency of communications networks. IBN employs a closed-loop architecture to monitor and optimize real-time network performance, reduce human intervention, and enhance resilience. However, the paradigm and the technological enablers introduce security challenges. This article studies the security gains and challenges in IBN from the aspect of enabling concepts and technologies. Furthermore, the article highlights potential solutions to existing challenges, outlines the standardization efforts, and summarizes the most important research gaps to advance future research in this direction.

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Section: A Backgroundmentioning

confidence: 99%

mentioning

confidence: 99%

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Security in Intent-Based Networking: Challenges and Solutions

Ahmad,

Malinen,

Christou

et al. 2023

2023 IEEE Conference on Standards for Communications and Networking (CSCN)

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“…The primary objective of an IBN is to establish an autonomic network by simplifying its management and operation. To this end, an IBN conceives the creation of a complete autonomous network framework [3], improving the robustness of the network and achieving dynamic operation and maintenance [4]. An IBN supports management functions guided by the use of intent.…”

Section: Introductionmentioning

confidence: 99%

“…It defines objectives and outcomes in a purely declarative manner, rather than specifying the detailed network configuration [5] or how to achieve it. Intents are then translated into network policies, which provide much more specific details concerning network configurations [3]. Such network policies will result in the execution of network actions, achieving the desired outcome specified in the intent.…”

Section: Introductionmentioning

confidence: 99%

Infrastructure-Wide and Intent-Based Networking Dataset for 5G-and-beyond AI-Driven Autonomous Networks

Andrade-Hoz,

Wang,

Alcaraz-Calero

2024

Sensors

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In the era of Autonomous Networks (ANs), artificial intelligence (AI) plays a crucial role for their development in cellular networks, especially in 5G-and-beyond networks. The availability of high-quality networking datasets is one of the essential aspects for creating data-driven algorithms in network management and optimisation tasks. These datasets serve as the foundation for empowering AI algorithms to make informed decisions and optimise network resources efficiently. In this research work, we propose the IW-IB-5GNET networking dataset: an infrastructure-wide and intent-based dataset that is intended to be of use in research and development of network management and optimisation solutions in 5G-and-beyond networks. It is infrastructure wide due to the fact that the dataset includes information from all layers of the 5G network. It is also intent based as it is initiated based on predefined user intents. The proposed dataset has been generated in an emulated 5G network, with a wide deployment of network sensors for its creation. The IW-IB-5GNET dataset is promising to facilitate the development of autonomous and intelligent network management solutions that enhance network performance and optimisation.

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Intent‐Based Network Configuration Using Large Language Models

Tu,

Nam,

Hong

2024

Int J Network Mgmt

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The increasing scale and complexity of network infrastructure present a huge challenge for network operators and administrators in performing network configuration and management tasks. Intent‐based networking has emerged as a solution to simplify the configuration and management of networks. However, one of the most difficult tasks of intent‐based networking is correctly translating high‐level natural language intents into low‐level network configurations. In this paper, we propose a general and effective approach to perform the network intent translation task using large language models with fine‐tuning, dynamic in‐context learning, and continuous learning. Fine‐tuning allows a pretrained large language model to perform better on a specific task. In‐context learning enables large language models to learn from the examples provided along with the actual intent. Continuous learning allows the system to improve overtime with new user intents. To demonstrate the feasibility of our approach, we present and evaluate it with two use cases: network formal specification translation and network function virtualization configuration. Our evaluation shows that with the proposed approach, we can achieve high intent translation accuracy as well as fast processing times using small large language models that can run on a single consumer‐grade GPU.

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A Survey on Intent-Based Networking

Cited by 78 publications

References 115 publications

Security in Intent-Based Networking: Challenges and Solutions

Security in Intent-Based Networking: Challenges and Solutions

Infrastructure-Wide and Intent-Based Networking Dataset for 5G-and-beyond AI-Driven Autonomous Networks

Intent‐Based Network Configuration Using Large Language Models

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