Paper Info
Title
Blockchain-Based Smart Home Networks Security Empowered with Fused Machine Learning
Abstract
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.
Year
DOI
Venue
2022
10.3390/s22124522
SENSORS
Keywords
DocType
Volume
Real-Time Sequential Deep Extreme Learning Machine, data fusion, blockchain, smart home
Journal
22
Issue
ISSN
Citations 
12
1424-8220
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Muhammad Sajid Farooq100.34
Safiullah Khan201.35
Abdur Rehman320.72
Sagheer Abbas411.03
Muhammad Adnan Khan598.71
Seong Oun Hwang613419.05