University of Hertfordshire

By the same authors

FIDS: A Federated Intrusion Detection System for 5G Smart Metering Network

Research output: Contribution to conferencePaperpeer-review

  • Parya Haji Mirzaee
  • Mohammad Shojafar
  • Zahra Pooranian
  • Pedram Asef
  • Haitham Cruickshank
  • Rahim Tafazoli
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Original languageEnglish
Pages1-9
Publication statusPublished - 15 Sep 2021
Event17th IEEE International Conference on Mobility, Sensing and Networking: IEEE MSN 2021 - Exeter, United Kingdom
Duration: 13 Dec 202115 Dec 2021
https://ieee-msn.org/2021/

Conference

Conference17th IEEE International Conference on Mobility, Sensing and Networking
Country/TerritoryUnited Kingdom
CityExeter
Period13/12/2115/12/21
Internet address

Abstract

In a critical infrastructure such as Smart Grid (SG), providing security of the system and privacy of consumers are significant challenges to be considered. The SG developers adopt Machine Learning (ML) algorithms within the Intrusion Detection System (IDS) to monitor traffic data and network performance. This visibility safeguards the SG from possible intrusions or attacks that may trigger the system. However, it requires access to residents' consumption information which is a severe threat to their privacy. In this paper, we present a novel method to detect abnormalities on a large scale SG while preserving the privacy of users. We design a Federated IDS (FIDS) architecture using Federated Learning (FL) in a 5G environment for the SG metering network. In this way, we design Federated Deep Neural Network (FDNN) model that protects customers' information and provides supervisory management for the whole energy distribution network. Simulation results for a real-time dataset demonstrate the reasonable improvement of the proposed FDNN model compared with the state-of-the-art algorithms. The FDNN achieves approximately 99.5% accuracy, 99.5% precision/recall, and 99.5% f1-score when comparing with classification algorithms.

ID: 26178251