A Machine Learning Approach for Blockchain-Based Smart Home Networks Security

被引:27
作者
Khan, Muhammad Adnan [1 ]
Abbas, Sagheer [2 ]
Rehman, Abdur [2 ]
Saeed, Yousaf [3 ]
Zeb, Asim [4 ]
Uddin, M. Irfan [5 ]
Nasser, Nidal [6 ]
Ali, Asmaa [7 ]
机构
[1] Riphah Int Univ, Dept Comp Sci, Fac Comp, Lahore Campus, Lahore, Pakistan
[2] NCBA&E, Sch Comp Sci, Lahore, Pakistan
[3] Univ Haripur, Dept Informat Technol, Haripur, Pakistan
[4] Abbotabad Univ Sci & Technol, Dept Comp Sci, Abbotabad, Pakistan
[5] Kohat Univ Sci & Technol, Inst Comp, Kohat, Pakistan
[6] Alfaisal Univ, Coll Engn, Software Engn, Riyadh, Saudi Arabia
[7] Queens Univ, Kingston, ON, Canada
来源
IEEE NETWORK | 2021年 / 35卷 / 03期
关键词
Blockchain; Smart homes; Internet of Things; Security; Smart cities; Safety; Peer-to-peer computing; CHALLENGES;
D O I
10.1109/MNET.011.2000530
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:223 / 229
页数:7
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