Open Access

ARTICLE

Logistic Regression with Elliptical Curve Cryptography to Establish Secure IoT

by J. R. Arunkumar^1,*, S. Velmurugan², Balarengadurai Chinnaiah³, G. Charulatha⁴, M. Ramkumar Prabhu⁴, A. Prabhu Chakkaravarthy⁵

1 Department of Information Technology, Sree Vidyanikethan Engineering College (Autonomous), Tirupati, Andhra Pradesh, India
2 Department of Electronics and Communication Engineering, TJS Engineering College, Kavaraipettai, Chennai, Tamil Nadu, India
3 Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India
4 Department of Electronics and Communication Engineering, Peri Institute of Technology, Chennai, Tamil Nadu, India
5 Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Science, Chennai, Tamil Nadu, India

* Corresponding Author: J. R. Arunkumar. Email:

Computer Systems Science and Engineering 2023, 45(3), 2635-2645. https://doi.org/10.32604/csse.2023.031605

Received 22 April 2022; Accepted 08 June 2022; Issue published 21 December 2022

Abstract

Nowadays, Wireless Sensor Network (WSN) is a modern technology with a wide range of applications and greatly attractive benefits, for example, self-governing, low expenditure on execution and data communication, long-term function, and unsupervised access to the network. The Internet of Things (IoT) is an attractive, exciting paradigm. By applying communication technologies in sensors and supervising features, WSNs have initiated communication between the IoT devices. Though IoT offers access to the highest amount of information collected through WSNs, it leads to privacy management problems. Hence, this paper provides a Logistic Regression machine learning with the Elliptical Curve Cryptography technique (LRECC) to establish a secure IoT structure for preventing, detecting, and mitigating threats. This approach uses the Elliptical Curve Cryptography (ECC) algorithm to generate and distribute security keys. ECC algorithm is a light weight key; thus, it minimizes the routing overhead. Furthermore, the Logistic Regression machine learning technique selects the transmitter based on intelligent results. The main application of this approach is smart cities. This approach provides continuing reliable routing paths with small overheads. In addition, route nodes cooperate with IoT, and it handles the resources proficiently and minimizes the 29.95% delay.

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Keywords

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