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Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning

The proliferation of the internet of things (IoT) technology has led to numerous challenges in various life domains, such as healthcare, smart systems, and mission-critical applications. The most critical issue is the security of IoT nodes, networks, and infrastructures. IoT uses the routing protoco...

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Autores principales: Zahra, F., Jhanjhi, NZ, Brohi, Sarfraz Nawaz, Khan, Navid Ali, Masud, Mehedi, AlZain, Mohammed A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501416/
https://www.ncbi.nlm.nih.gov/pubmed/36146111
http://dx.doi.org/10.3390/s22186765
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author Zahra, F.
Jhanjhi, NZ
Brohi, Sarfraz Nawaz
Khan, Navid Ali
Masud, Mehedi
AlZain, Mohammed A.
author_facet Zahra, F.
Jhanjhi, NZ
Brohi, Sarfraz Nawaz
Khan, Navid Ali
Masud, Mehedi
AlZain, Mohammed A.
author_sort Zahra, F.
collection PubMed
description The proliferation of the internet of things (IoT) technology has led to numerous challenges in various life domains, such as healthcare, smart systems, and mission-critical applications. The most critical issue is the security of IoT nodes, networks, and infrastructures. IoT uses the routing protocol for low-power and lossy networks (RPL) for data communication among the devices. RPL comprises a lightweight core and thus does not support high computation and resource-consuming methods for security implementation. Therefore, both IoT and RPL are vulnerable to security attacks, which are broadly categorized into RPL-specific and sensor-network-inherited attacks. Among the most concerning protocol-specific attacks are rank attacks and wormhole attacks in sensor-network-inherited attack types. They target the RPL resources and components including control messages, repair mechanisms, routing topologies, and sensor network resources by consuming. This leads to the collapse of IoT infrastructure. In this paper, a lightweight multiclass classification-based RPL-specific and sensor-network-inherited attack detection model called MC-MLGBM is proposed. A novel dataset was generated through the construction of various network models to address the unavailability of the required dataset, optimal feature selection to improve model performance, and a light gradient boosting machine-based algorithm optimized for a multiclass classification-based attack detection. The results of extensive experiments are demonstrated through several metrics including confusion matrix, accuracy, precision, and recall. For further performance evaluation and to remove any bias, the multiclass-specific metrics were also used to evaluate the model, including cross-entropy, Cohn’s kappa, and Matthews correlation coefficient, and then compared with benchmark research.
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spelling pubmed-95014162022-09-24 Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning Zahra, F. Jhanjhi, NZ Brohi, Sarfraz Nawaz Khan, Navid Ali Masud, Mehedi AlZain, Mohammed A. Sensors (Basel) Article The proliferation of the internet of things (IoT) technology has led to numerous challenges in various life domains, such as healthcare, smart systems, and mission-critical applications. The most critical issue is the security of IoT nodes, networks, and infrastructures. IoT uses the routing protocol for low-power and lossy networks (RPL) for data communication among the devices. RPL comprises a lightweight core and thus does not support high computation and resource-consuming methods for security implementation. Therefore, both IoT and RPL are vulnerable to security attacks, which are broadly categorized into RPL-specific and sensor-network-inherited attacks. Among the most concerning protocol-specific attacks are rank attacks and wormhole attacks in sensor-network-inherited attack types. They target the RPL resources and components including control messages, repair mechanisms, routing topologies, and sensor network resources by consuming. This leads to the collapse of IoT infrastructure. In this paper, a lightweight multiclass classification-based RPL-specific and sensor-network-inherited attack detection model called MC-MLGBM is proposed. A novel dataset was generated through the construction of various network models to address the unavailability of the required dataset, optimal feature selection to improve model performance, and a light gradient boosting machine-based algorithm optimized for a multiclass classification-based attack detection. The results of extensive experiments are demonstrated through several metrics including confusion matrix, accuracy, precision, and recall. For further performance evaluation and to remove any bias, the multiclass-specific metrics were also used to evaluate the model, including cross-entropy, Cohn’s kappa, and Matthews correlation coefficient, and then compared with benchmark research. MDPI 2022-09-07 /pmc/articles/PMC9501416/ /pubmed/36146111 http://dx.doi.org/10.3390/s22186765 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
Zahra, F.
Jhanjhi, NZ
Brohi, Sarfraz Nawaz
Khan, Navid Ali
Masud, Mehedi
AlZain, Mohammed A.
Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
title Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
title_full Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
title_fullStr Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
title_full_unstemmed Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
title_short Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
title_sort rank and wormhole attack detection model for rpl-based internet of things using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501416/
https://www.ncbi.nlm.nih.gov/pubmed/36146111
http://dx.doi.org/10.3390/s22186765
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