Edge Device Identification Based on Federated Learning and Network Traffic Feature Engineering

Zhimin He, Jie Yin, Yu Wang, Guan Gui, Bamidele Adebisi, Tomoaki Ohtsuki, Haris Gacanin, Hikmet Sari

Research output: Contribution to journalArticlepeer-review

Abstract

With the ubiquitous deployment and applications of internet of things (IoT), security issues pose a critical challenge to IoT devices. External attackers often utilize vulnerable IoT devices to invade the target’s internal network and then further cause a security threat to the whole network. To prevent such attacks, it is necessary to develop a security mechanism to control the access of suspicious IoT devices and manage the internal devices. In recent years, deep learning (DL) algorithm has been widely used in the field of edge device identification (EDI), and has made great achievements. However, these previous methods are essentially centralized learning-based EDI (CentEDI) that trains all data together, which can not guarantee data security and not conducive to deployment on edge devices. To address this problem, we introduce a federated learning-based EDI (FedeEDI) method via network traffic to automatically identify edge devices connected to the whole network. Experimental results show that the training efficiency of our proposed FedeEDI method is much higher than that of the CentEDI method, although its classification accuracy is slightly reduced. In contrast to the CentEDI method, the proposed FedeEDI method has two main advantages: faster training speed and safer training process.

Original languageEnglish
JournalIEEE Transactions on Cognitive Communications and Networking
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • centralized learning
  • Deep learning
  • deep learning.
  • edge device identification
  • Feature extraction
  • federated learning
  • Internet of things
  • Internet of Things
  • Machine learning algorithms
  • network traffic
  • Object recognition
  • Security
  • Training

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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