Optimal Machine Learning Based Privacy Preserving Blockchain Assisted Internet of Things with Smart Cities Environment

Currently, the amount of Internet of Things (IoT) applications is enhanced for processing, analyzing, and managing the created big data from the smart city. Certain other applications of smart cities were location-based services, transportation management, and urban design, amongst others. There are...

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Veröffentlicht in:Applied sciences Jg. 12; H. 12; S. 5893
Hauptverfasser: Al-Qarafi, A., Alrowais, Fadwa, S. Alotaibi, Saud, Nemri, Nadhem, Al-Wesabi, Fahd N., Al Duhayyim, Mesfer, Marzouk, Radwa, Othman, Mahmoud, Al-Shabi, M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2022
Schlagworte:
Algorithms
Assaults
Blockchain
blockchain assisted IoT
Data transmission
Design
Feature selection
Internet of Things
intrusion detection
Machine learning
Optimization
Principal components analysis
Privacy
privacy preserving
security
Smart cities
smart city
Technology
ISSN:2076-3417, 2076-3417
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Abstract Currently, the amount of Internet of Things (IoT) applications is enhanced for processing, analyzing, and managing the created big data from the smart city. Certain other applications of smart cities were location-based services, transportation management, and urban design, amongst others. There are several challenges under these applications containing privacy, data security, mining, and visualization. The blockchain-assisted IoT application (BIoT) is offering new urban computing to secure smart cities. The blockchain is a secure and transparent data-sharing decentralized platform, so BIoT is suggested as the optimum solution to the aforementioned challenges. In this view, this study develops an Optimal Machine Learning-based Intrusion Detection System for Privacy Preserving BIoT with Smart Cities Environment, called OMLIDS-PBIoT technique. The presented OMLIDS-PBIoT technique exploits BC and ML techniques to accomplish security in the smart city environment. For attaining this, the presented OMLIDS-PBIoT technique employs data pre-processing in the initial stage to transform the data into a compatible format. Moreover, a golden eagle optimization (GEO)-based feature selection (FS) model is designed to derive useful feature subsets. In addition, a heap-based optimizer (HBO) with random vector functional link network (RVFL) model was utilized for intrusion classification. Additionally, blockchain technology is exploited for secure data transmission in the IoT-enabled smart city environment. The performance validation of the OMLIDS-PBIoT technique is carried out using benchmark datasets, and the outcomes are inspected under numerous factors. The experimental results demonstrate the superiority of the OMLIDS-PBIoT technique over recent approaches.
AbstractList Currently, the amount of Internet of Things (IoT) applications is enhanced for processing, analyzing, and managing the created big data from the smart city. Certain other applications of smart cities were location-based services, transportation management, and urban design, amongst others. There are several challenges under these applications containing privacy, data security, mining, and visualization. The blockchain-assisted IoT application (BIoT) is offering new urban computing to secure smart cities. The blockchain is a secure and transparent data-sharing decentralized platform, so BIoT is suggested as the optimum solution to the aforementioned challenges. In this view, this study develops an Optimal Machine Learning-based Intrusion Detection System for Privacy Preserving BIoT with Smart Cities Environment, called OMLIDS-PBIoT technique. The presented OMLIDS-PBIoT technique exploits BC and ML techniques to accomplish security in the smart city environment. For attaining this, the presented OMLIDS-PBIoT technique employs data pre-processing in the initial stage to transform the data into a compatible format. Moreover, a golden eagle optimization (GEO)-based feature selection (FS) model is designed to derive useful feature subsets. In addition, a heap-based optimizer (HBO) with random vector functional link network (RVFL) model was utilized for intrusion classification. Additionally, blockchain technology is exploited for secure data transmission in the IoT-enabled smart city environment. The performance validation of the OMLIDS-PBIoT technique is carried out using benchmark datasets, and the outcomes are inspected under numerous factors. The experimental results demonstrate the superiority of the OMLIDS-PBIoT technique over recent approaches.
Author Nemri, Nadhem
Al-Qarafi, A.
Alrowais, Fadwa
Al Duhayyim, Mesfer
Al-Wesabi, Fahd N.
Al-Shabi, M.
Marzouk, Radwa
S. Alotaibi, Saud
Othman, Mahmoud
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SubjectTerms Algorithms
Assaults
Blockchain
blockchain assisted IoT
Data transmission
Design
Feature selection
Internet of Things
intrusion detection
Machine learning
Optimization
Principal components analysis
Privacy
privacy preserving
security
Smart cities
smart city
Technology
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Title Optimal Machine Learning Based Privacy Preserving Blockchain Assisted Internet of Things with Smart Cities Environment
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