On Cooperative Federated Defense to Secure Multi-Access Edge Computing

Hichem Sedjelmaci, Nirwan Ansari

Research output: Contribution to specialist publicationArticle

Abstract

The research of cyber security for Multi-access Edge Computing (MEC) has not yet received a great interest. Specifically, the attack detection issue is considered as a major concern since the MEC network, which handles attractive information, is prone to several internal and external network attacks. Recently, the Federated Learning (FL) and Generative Adversarial Network (GAN) have been used to detect and prevent attacks from targeting the wireless mobile networks. In this article, we present the state of the art of MEC attack detection and defense frameworks which incorporate FL and GAN algorithms. Moreover, we propose a new cyber defense framework based on a Federated Generative Adversarial Network (FedGAN) algorithm and non-cooperative game to improve over time the precision of attack detection.

Original languageEnglish (US)
Specialist publicationIEEE Consumer Electronics Magazine
DOIs
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • FL
  • GAN and Game theory
  • Generative adversarial networks
  • Image edge detection
  • MEC
  • Mobile handsets
  • Monitoring
  • Security
  • Security
  • Servers
  • Training

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