An Efficient Intrusion Detection Model for Edge System in Brownfield Industrial Internet of Things

Muna AL-Hawawreh, Elena Sitnikova, Frank Den Hartog

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

13 Citations (Scopus)

Abstract

The Industrial Internet of Things (IIoT) is bringing control systems online leading to significant innovation in industry and business. However, this development also comes with new cybersecurity threats. As much of the value of IIoT systems resides at the edge tier, this makes them potentially desired targets for attackers. Protecting edge physical systems by monitoring them and identifying malicious activities based on an efficient detection model is therefore of utmost importance. This paper proposes a detection model based on deep learning techniques that can learn and test using data collected from Remote Telemetry Unit (RTU) streams of gas pipeline system. It utilizes the sparse and denoising auto-encoder methods for unsupervised learning and deep neural networks for supervised learning to produce a high-level data representation from unlabeled and noisy data. Our results show that the proposed model achieves superior performance in identifying malicious activities.

Original languageEnglish
Title of host publicationBDIOT 2019 - 3rd International Conference on Big Data and Internet of Things
PublisherAssociation for Computing Machinery
Pages83-87
Number of pages5
ISBN (Electronic)9781450372466
DOIs
Publication statusPublished - 22 Aug 2019
Externally publishedYes
Event3rd International Conference on Big Data and Internet of Things, BDIOT 2019 - Melbourne, Australia
Duration: 22 Aug 201924 Aug 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Big Data and Internet of Things, BDIOT 2019
Country/TerritoryAustralia
CityMelbourne
Period22/08/1924/08/19

Keywords

  • Brownfield
  • Cybersecurity
  • Deep learning
  • Edge system
  • IDS

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