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A Machine Learning Approach for Blockchain-Based Smart Home Networks Security
- Source :
- IEEE Network. 35:223-229
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Realizing secure and private communications on the Internet of Things (IoT) is challenging, primarily due to IoT's projected vast scale and extensive deployment. Recent efforts have explored the use of blockchain in decentralized protection and privacy supported. Such solutions, however, are highly demanding in terms of computation and time requirements, barring these solutions from the majority of IoT applications. Specifically, in this paper, we introduce a resource-efficient, blockchain-based solution for secure and private IoT. The solution is made possible through novel exploitation of computational resources in a typical IoT environment (e.g., smart homes), along with the use of an instance of Deep Extreme Learning Machine (DELM). In this proposed approach, the Smart Home Architecture based in Blockchain is protected by carefully evaluating its reliability in regard to the essential security aims of privacy, integrity, and accessibility. In addition, we present simulation results to emphasize that the overheads created by our method (in terms of distribution, processing time, and energy consumption) are marginal related to their protection and privacy benefits.
- Subjects :
- Blockchain
Computer Networks and Communications
Computer science
business.industry
Scale (chemistry)
Reliability (computer networking)
020206 networking & telecommunications
02 engineering and technology
Energy consumption
Computer security
computer.software_genre
Hardware and Architecture
Home automation
Software deployment
0202 electrical engineering, electronic engineering, information engineering
Architecture
business
computer
Software
Information Systems
Extreme learning machine
Subjects
Details
- ISSN :
- 1558156X and 08908044
- Volume :
- 35
- Database :
- OpenAIRE
- Journal :
- IEEE Network
- Accession number :
- edsair.doi...........c63914c3a7961b35da5aa5a0fc5fe4da
- Full Text :
- https://doi.org/10.1109/mnet.011.2000514