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Cryptographic Algorithm for Enhancing Data Security in Wireless IoT Sensor Networks

A. Bhavani, V. Nithya*

Department of ECE, SRM Institute of Science and Technology, Kattankulathur, 603203, Chennai, Tamilnadu, India

* Corresponding Author: V. Nithya. Email: email

Intelligent Automation & Soft Computing 2023, 36(2), 1381-1393. https://doi.org/10.32604/iasc.2023.029397

Abstract

Wireless IoT Sensor Network can handle audio, video, text, etc., through the interconnection of ubiquitous devices. The entertainment and application-centric network relies on its autonomous nodes for handling large streams of multimedia data. Security breaches and threats due to insider attacks reduce the data handling and distribution capacity of the nodes. For addressing the insider attacks problem, Session-Critical Distributed Authentication Method (SCDAM) is proposed. The proposed method relies on short-lived concealed authentication based on an improved elliptic curve cryptography (ECC) algorithm. In this authentication, the session time and the interrupts are accounted for, providing end-to-end authentication. The session keys are distributed before and after each interrupt from which the shared data is authenticated. This authentication process uses a linear hash process, ensuring non-repetition of keys in the consecutive sessions. This proposed authentication method is capable of providing improved sessions, less data distribution loss, and complexity.

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APA Style
Bhavani, A., Nithya, V. (2023). Cryptographic algorithm for enhancing data security in wireless iot sensor networks. Intelligent Automation & Soft Computing, 36(2), 1381-1393. https://doi.org/10.32604/iasc.2023.029397
Vancouver Style
Bhavani A, Nithya V. Cryptographic algorithm for enhancing data security in wireless iot sensor networks. Intell Automat Soft Comput . 2023;36(2):1381-1393 https://doi.org/10.32604/iasc.2023.029397
IEEE Style
A. Bhavani and V. Nithya, “Cryptographic Algorithm for Enhancing Data Security in Wireless IoT Sensor Networks,” Intell. Automat. Soft Comput. , vol. 36, no. 2, pp. 1381-1393, 2023. https://doi.org/10.32604/iasc.2023.029397



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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