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Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID

Technological breakthroughs in the Internet of Things (IoT) easily promote smart lives for humans by connecting everything through the Internet. The de facto standardised IoT routing strategy is the routing protocol for low-power and lossy networks (RPL), which is applied in various heterogeneous Io...

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Autores principales: Abosata, Nasr, Al-Rubaye, Saba, Inalhan, Gokhan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824493/
https://www.ncbi.nlm.nih.gov/pubmed/36616920
http://dx.doi.org/10.3390/s23010321
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author Abosata, Nasr
Al-Rubaye, Saba
Inalhan, Gokhan
author_facet Abosata, Nasr
Al-Rubaye, Saba
Inalhan, Gokhan
author_sort Abosata, Nasr
collection PubMed
description Technological breakthroughs in the Internet of Things (IoT) easily promote smart lives for humans by connecting everything through the Internet. The de facto standardised IoT routing strategy is the routing protocol for low-power and lossy networks (RPL), which is applied in various heterogeneous IoT applications. Hence, the increase in reliance on the IoT requires focus on the security of the RPL protocol. The top defence layer is an intrusion detection system (IDS), and the heterogeneous characteristics of the IoT and variety of novel intrusions make the design of the RPL IDS significantly complex. Most existing IDS solutions are unified models and cannot detect novel RPL intrusions. Therefore, the RPL requires a customised global attack knowledge-based IDS model to identify both existing and novel intrusions in order to enhance its security. Federated transfer learning (FTL) is a trending topic that paves the way to designing a customised RPL-IoT IDS security model in a heterogeneous IoT environment. In this paper, we propose a federated-transfer-learning-assisted customised distributed IDS (FT-CID) model to detect RPL intrusion in a heterogeneous IoT. The design process of FT-CID includes three steps: dataset collection, FTL-assisted edge IDS learning, and intrusion detection. Initially, the central server initialises the FT-CID with a predefined learning model and observes the unique features of different RPL-IoTs to construct a local model. The experimental model generates an RPL-IIoT dataset with normal and abnormal traffic through simulation on the Contiki-NG OS. Secondly, the edge IDSs are trained using the local parameters and the globally shared parameters generated by the central server through federation and aggregation of different local parameters of various edges. Hence, transfer learning is exploited to update the server’s and edges’ local and global parameters based on relational knowledge. It also builds and customised IDS model with partial retraining through local learning based on globally shared server knowledge. Finally, the customised IDS in the FT-CID model enforces the detection of intrusions in heterogeneous IoT networks. Moreover, the FT-CID model accomplishes high RPL security by implicitly utilising the local and global parameters of different IoTs with the assistance of FTL. The FT-CID detects RPL intrusions with an accuracy of 85.52% in tests on a heterogeneous IoT network.
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spelling pubmed-98244932023-01-08 Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID Abosata, Nasr Al-Rubaye, Saba Inalhan, Gokhan Sensors (Basel) Article Technological breakthroughs in the Internet of Things (IoT) easily promote smart lives for humans by connecting everything through the Internet. The de facto standardised IoT routing strategy is the routing protocol for low-power and lossy networks (RPL), which is applied in various heterogeneous IoT applications. Hence, the increase in reliance on the IoT requires focus on the security of the RPL protocol. The top defence layer is an intrusion detection system (IDS), and the heterogeneous characteristics of the IoT and variety of novel intrusions make the design of the RPL IDS significantly complex. Most existing IDS solutions are unified models and cannot detect novel RPL intrusions. Therefore, the RPL requires a customised global attack knowledge-based IDS model to identify both existing and novel intrusions in order to enhance its security. Federated transfer learning (FTL) is a trending topic that paves the way to designing a customised RPL-IoT IDS security model in a heterogeneous IoT environment. In this paper, we propose a federated-transfer-learning-assisted customised distributed IDS (FT-CID) model to detect RPL intrusion in a heterogeneous IoT. The design process of FT-CID includes three steps: dataset collection, FTL-assisted edge IDS learning, and intrusion detection. Initially, the central server initialises the FT-CID with a predefined learning model and observes the unique features of different RPL-IoTs to construct a local model. The experimental model generates an RPL-IIoT dataset with normal and abnormal traffic through simulation on the Contiki-NG OS. Secondly, the edge IDSs are trained using the local parameters and the globally shared parameters generated by the central server through federation and aggregation of different local parameters of various edges. Hence, transfer learning is exploited to update the server’s and edges’ local and global parameters based on relational knowledge. It also builds and customised IDS model with partial retraining through local learning based on globally shared server knowledge. Finally, the customised IDS in the FT-CID model enforces the detection of intrusions in heterogeneous IoT networks. Moreover, the FT-CID model accomplishes high RPL security by implicitly utilising the local and global parameters of different IoTs with the assistance of FTL. The FT-CID detects RPL intrusions with an accuracy of 85.52% in tests on a heterogeneous IoT network. MDPI 2022-12-28 /pmc/articles/PMC9824493/ /pubmed/36616920 http://dx.doi.org/10.3390/s23010321 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Abosata, Nasr
Al-Rubaye, Saba
Inalhan, Gokhan
Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID
title Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID
title_full Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID
title_fullStr Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID
title_full_unstemmed Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID
title_short Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID
title_sort customised intrusion detection for an industrial iot heterogeneous network based on machine learning algorithms called ftl-cid
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9824493/
https://www.ncbi.nlm.nih.gov/pubmed/36616920
http://dx.doi.org/10.3390/s23010321
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