University of Oulu

Hireche, O., Benzaïd, C., & Taleb, T. (2022). Deep data plane programming and AI for zero-trust self-driven networking in beyond 5G. Computer Networks, 203, 108668.

Deep data plane programming and AI for zero-trust self-driven networking in beyond 5G

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Author: Hireche, Othmane1; Benzaïd, Chafika1; Taleb, Tarik2,3
Organizations: 1Aalto University, Espoo, Finland
2University of Oulu, Oulu, Finland
3Sejong University, Seoul, South Korea
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.2 MB)
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Language: English
Published: Elsevier, 2022
Publish Date: 2022-07-07


Along with the high demand for network connectivity from both end-users and service providers, networks have become highly complex; and so has become their lifecycle management. Recent advances in automation, data analysis, artificial intelligence, distributed ledger technologies (e.g., Blockchain), and data plane programming techniques have sparked the hope of the researchers’ community in exploring and leveraging these techniques towards realizing the much-needed vision of trustworthy self-driving networks (SelfDNs). In this vein, this article proposes a novel framework to empower fully distributed trustworthy SelfDNs across multiple domains. The framework vision is achieved by exploiting (i) the capabilities of programmable data planes to enable real-time in-network telemetry collection; (ii) the potential of P4 — as an important example of data plane programming languages — and AI to (re)write the source code of network components in a fashion that the network becomes capable of automatically translating a policy intent into executable actions that can be enforced on the network components; and (iii) the potential of blockchain and federated learning to enable decentralized, secure and trustable knowledge sharing between domains. A relevant use case is introduced and discussed to demonstrate the feasibility of the intended vision. Encouraging results are obtained and discussed.

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Series: Computer networks. The international journal of computer and telecommunications networking
ISSN: 1389-1286
ISSN-E: 1872-7069
ISSN-L: 1389-1286
Volume: 203
Article number: 108668
DOI: 10.1016/j.comnet.2021.108668
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported in part by the European Union’s Horizon 2020 research and innovation programme under the MonB5G project (Grant No. 871780); and the Academy of Finland Project 6Genesis Flagship (Grant No. 318927).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
Copyright information: © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (