DRL-based Federated Uncertainty-guided Semi-Supervised Learning for Network Traffic Selection and Threshold Determination in ZSM

Abdullatif Albaseer, Mohamed Abdallah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

The ever-expanding landscape of advanced applications and services, as well as the associated emerging attacks in the zero-touch network and service management (ZSM) paradigm, necessitates novel approaches to manage complex network infrastructures while addressing the security requirements of beyond 5G networks. To address this issue, we present a cutting-edge, novel semi-supervised federated learning approach that incorporates a Deep Reinforcement Learning (DRL) agent for real-time defense system updates. Specifically, we propose the DRL-FedUSS framework, which stands for DRL-based Federated Uncertainty-guided Semi-Supervised learning. DRL-FedUSS is designed explicitly for Label-at-Client scenarios to accelerate the training convergence when clients hold a scarcity of labeled and an abundance of unlabeled network traffic samples. The DRL-FedUSS framework integrates a DRL agent that intelligently selects the most informative samples with a real-time adaptive threshold for data annotation, considering uncertainty, time, budget constraints, and, most importantly, the convergence rate and confidence level constraints. Our extensive simulations on realistic non-independent and identically distributed (non-IID) datasets prove that the DRL-FedUSS framework outperforms baseline approaches, achieving superior intrusion detection accuracy, reducing the associated cost, and accelerating the convergence rate with minimal network traffic labeled data.

Original languageEnglish
Title of host publicationGLOBECOM 2023 - 2023 IEEE Global Communications Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1253-1258
Number of pages6
ISBN (Electronic)9798350310900
DOIs
Publication statusPublished - 2023
Event2023 IEEE Global Communications Conference, GLOBECOM 2023 - Kuala Lumpur, Malaysia
Duration: 4 Dec 20238 Dec 2023

Publication series

NameProceedings - IEEE Global Communications Conference, GLOBECOM
ISSN (Print)2334-0983
ISSN (Electronic)2576-6813

Conference

Conference2023 IEEE Global Communications Conference, GLOBECOM 2023
Country/TerritoryMalaysia
CityKuala Lumpur
Period4/12/238/12/23

Keywords

  • DRL
  • Intrusion Detection
  • Next Generation Networks
  • Semi-supervised Federated learning
  • Threat Landscape

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