Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning
Security and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private blockchain-based smart home network architecture fo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/12/4522 |
| _version_ | 1827656696134107136 |
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| author | Muhammad Sajid Farooq Safiullah Khan Abdur Rehman Sagheer Abbas Muhammad Adnan Khan Seong Oun Hwang |
| author_facet | Muhammad Sajid Farooq Safiullah Khan Abdur Rehman Sagheer Abbas Muhammad Adnan Khan Seong Oun Hwang |
| author_sort | Muhammad Sajid Farooq |
| collection | DOAJ |
| description | Security and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private blockchain-based smart home network architecture for estimating intrusion detection empowered with a Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system model is proposed. This study investigates the methodology of RTS-DELM implemented in blockchain-based smart homes to detect any malicious activity. The approach of data fusion and the decision level fusion technique are also implemented to achieve enhanced accuracy. This study examines the numerous key components and features of the smart home network framework more extensively. The Fused RTS-DELM technique achieves a very significant level of stability with a low error rate for any intrusion activity in smart home networks. The simulation findings indicate that this suggested technique successfully optimizes smart home networks for monitoring and detecting harmful or intrusive activities. |
| first_indexed | 2024-03-09T22:32:00Z |
| format | Article |
| id | doaj.art-7bb2daff4bce462e8ac508cea8ed7f2a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:32:00Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7bb2daff4bce462e8ac508cea8ed7f2a2023-11-23T18:54:43ZengMDPI AGSensors1424-82202022-06-012212452210.3390/s22124522Blockchain-Based Smart Home Networks Security Empowered with Fused Machine LearningMuhammad Sajid Farooq0Safiullah Khan1Abdur Rehman2Sagheer Abbas3Muhammad Adnan Khan4Seong Oun Hwang5School of Computer Science, National College of Business Administration & Economics, Lahore 54000, PakistanDepartment of IT Convergence Engineering, Gachon University, Seongnam 13120, KoreaSchool of Computer Science, National College of Business Administration & Economics, Lahore 54000, PakistanSchool of Computer Science, National College of Business Administration & Economics, Lahore 54000, PakistanPattern Recognition and Machine Learning Lab, Department of Software, Gachon University, Seongnam 13557, KoreaDepartment of Computer Engineering, Gachon University, Seongnam 13120, KoreaSecurity and privacy in the Internet of Things (IoT) other significant challenges, primarily because of the vast scale and deployment of IoT networks. Blockchain-based solutions support decentralized protection and privacy. In this study, a private blockchain-based smart home network architecture for estimating intrusion detection empowered with a Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system model is proposed. This study investigates the methodology of RTS-DELM implemented in blockchain-based smart homes to detect any malicious activity. The approach of data fusion and the decision level fusion technique are also implemented to achieve enhanced accuracy. This study examines the numerous key components and features of the smart home network framework more extensively. The Fused RTS-DELM technique achieves a very significant level of stability with a low error rate for any intrusion activity in smart home networks. The simulation findings indicate that this suggested technique successfully optimizes smart home networks for monitoring and detecting harmful or intrusive activities.https://www.mdpi.com/1424-8220/22/12/4522Real-Time Sequential Deep Extreme Learning Machinedata fusionblockchainsmart home |
| spellingShingle | Muhammad Sajid Farooq Safiullah Khan Abdur Rehman Sagheer Abbas Muhammad Adnan Khan Seong Oun Hwang Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning Sensors Real-Time Sequential Deep Extreme Learning Machine data fusion blockchain smart home |
| title | Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning |
| title_full | Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning |
| title_fullStr | Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning |
| title_full_unstemmed | Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning |
| title_short | Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning |
| title_sort | blockchain based smart home networks security empowered with fused machine learning |
| topic | Real-Time Sequential Deep Extreme Learning Machine data fusion blockchain smart home |
| url | https://www.mdpi.com/1424-8220/22/12/4522 |
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