On resource consumption of machine learning in communications network security

Aug 5, 2026

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As communication networks grow more complex through expanding devices, services, and applications, machine learning (ML) adoption for management and security has increased substantially. In communications network security, ML supports preventive measures as well as detection and remediation by learning and adapting to evolving threat landscapes. Resource demands remain a key limitation because ML depends on continuously rising data volumes. This review investigates resource consumption in ML-based network security methods and proposes a taxonomy to classify approaches that reduce energy, computing, memory, latency, bandwidth, and human effort.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages34

Reading Time1 h 26 min

AccessFree to download

Introduction

Background and motivation for ML in network security

Shift toward automated, ML-based security

This document is downloaded from the VTT Research Information Portal [https://cris.vtt.fi](https://cris.vtt.fi) VTT Technical Research Centre of Finland On resource consumption of machine learning in communications network security Hoque, Md Muzammal; Ahmad, Ijaz; Suomalainen, Jani; Dini, Paolo; Tahir, Mohammad Published in: Computer Networks DOI: 10.1016/j.comnet.2025.111600 Published: 01/08/2025 Document Version Publisher's final version License CC BY Link to publication Please cite the original version: Hoque, M. M. , Ahmad, I. , Suomalainen, J. , Dini, P. , & Tahir, M. (2025) . On resource consumption of machine learning in communications network security. Computer Networks, 271 , Article 111600. [https://doi.org/10.1016/j.comnet.2025.111600](https://doi.org/10.1016/j.comnet.2025.111600) VTT [https://www.vttresearch.com](https://www.vttresearch.com) VTT Technical Research Centre of Finland Ltd [P.O. box 1000](P.O. box 1000)[ ](P.O. box 1000)[FI-02044 VTT](FI-02044 VTT)[ ](FI-02044 VTT)Finland By using VTT Research Information Portal you are bound by the following Terms & Conditions. I have read and I understand the following statement: This document is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of this document is not permitted, except duplication for research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered for sale. Download date: 08. Mar. 2026 | On resource consumption of machine learning in communications network security
Md Muzammal Hoque a ,∗, Ijaz Ahmad a, Jani Suomalainen a, Paolo Dinib, Mohammad Tahir c
a VTT Technical Research Centre of Finland, FI-02044, Espoo, Finland
b CTTC/CERCA, 08860, Barcelona, Spain
c University of Turku, Department of Computing, FI-20014, Turku, Finland | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords: Security 6G
Distributed security Network security Resource consumption
Resource efficiency Machine learning ML
6G security Sustainability DNN | | As the complexity of communication networks continues to increase, driven by a diverse array of devices, services and applications, the adoption of Machine Learning (ML) has seen a significant rise to address various challenges ranging from management to security. Regarding network security, the application of ML ranges from preventive measures to detection and remediation due to its ability to dynamically learn and adapt to evolving threat landscapes. However, ML requires a significant amount of resources, mainly due to the fact that ML operates on data, and the volumes of data are consistently rising. This review article explores the resource consumption aspect of ML techniques used for network security and provides a comprehensive review of the current state of research. Moreover, we propose a taxonomy that can be used to classify the methods through which the resource consumption can be reduced for different ML-based network security implementations. The focus of the study encompasses several key aspects related to resource consumption, including energy, computing, memory, latency, bandwidth, and human resources. These resources are critical in improving the efficiency and optimizing the reliability and sustainability of network security solutions. Furthermore, based on an extensive literature review, we summarize key points regarding optimizing resource consumption in MLbased network security solutions. Finally, the challenges and future research directions for resource-efficient, ML-based network security solutions are outlined to aid in the advancement of research in this area. | 1. Introduction Communication networks, including wireless, fixed, and nonterrestrial networks, are integrating new and diverse technologies to meet the growing demands of emerging services an

FAQs

What does the review contribute to the research area?

It surveys current work on resource consumption across ML techniques for network security and proposes a taxonomy to classify methods that reduce resource consumption for different ML-based network security deployments.

Aug 5, 2026

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