An efficient approach for enhancing security in Internet of Things using the optimum authentication key

被引：27

作者：

Kalyani G. ^{[1
,2
]}

Chaudhari S. ^{[3
]}

机构：

[1] Research Center, School of C&IT, Reva University, Bangalore

[2] Department of CSE, SNIST, Hyderabad

[3] Department of Computer Science and Engineering, M.S Ramaiah Institute of Technology, Bangalore

来源：

International Journal of Computers and Applications | 2020年 / 42卷 / 03期

关键词：

classification; DNN; Homomorphic Encryption; IoT; sensitive data; SFF;

D O I：

10.1080/1206212X.2019.1619277

中图分类号：

学科分类号：

摘要：

Nowadays, Internet of Things (IoT) is turning into an attractive framework to drive a substantive jump on merchandise and enterprises through physical, digital, and social spaces. This paper enhances IoT security authentication by utilizing cryptographic-based methodologies. In this study, we secure IoT sensitive data with the help of Optimal Homomorphic Encryption (OHE) with high dependability. Sensitive data from IoT dataset are classified based on Deep Learning Neural Network structure (DNN). After classification, OHE performs sensitive data in the process of encryption and decryption. During encryption, the key is authenticated and the optimal key is selected by using Step size Fire Fly (SFF) optimization algorithm. This strategy can build up the encrypted key and attains the most prominent privacy-preserving data in IoT. The outcome shows that the performance of the proposed IoT security model achieves maximum key breaking time and less computational time with high security. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

引用

页码：306 / 314

页数：8

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