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Review

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

1
Department of Computer Science, University of Idaho, Moscow, ID 83844, USA
2
Department of ECE, Science, University of Idaho, Moscow, ID 83844, USA
3
Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
*
Author to whom correspondence should be addressed.
Retired.
Academic Editors: Ethiopia Nigussie and Habtamu Abie
Sensors 2021, 21(19), 6432; https://doi.org/10.3390/s21196432
Received: 15 August 2021 / Revised: 14 September 2021 / Accepted: 21 September 2021 / Published: 26 September 2021
(This article belongs to the Special Issue Security and Trustworthiness in Industrial IoT)
This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets). View Full-Text
Keywords: anomaly-based IDS; IoT architecture mapping; deep learning; machine learning (ML); intrusion-detection systems (IDS); IoT security anomaly-based IDS; IoT architecture mapping; deep learning; machine learning (ML); intrusion-detection systems (IDS); IoT security
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MDPI and ACS Style

Albulayhi, K.; Smadi, A.A.; Sheldon, F.T.; Abercrombie, R.K. IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses. Sensors 2021, 21, 6432. https://doi.org/10.3390/s21196432

AMA Style

Albulayhi K, Smadi AA, Sheldon FT, Abercrombie RK. IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses. Sensors. 2021; 21(19):6432. https://doi.org/10.3390/s21196432

Chicago/Turabian Style

Albulayhi, Khalid, Abdallah A. Smadi, Frederick T. Sheldon, and Robert K. Abercrombie 2021. "IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses" Sensors 21, no. 19: 6432. https://doi.org/10.3390/s21196432

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