Open Access iconOpen Access

ARTICLE

crossmark

Feature Selection Based on IoT Aware QDA Node Authentication in 5G Networks

M. P. Haripriya*, P. Venkadesh

Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, 629180, India

* Corresponding Author: M. P. Haripriya. Email: email

Intelligent Automation & Soft Computing 2022, 33(2), 825-836. https://doi.org/10.32604/iasc.2022.022940

Abstract

The coming generation in mobile networks is the fifth generation (5G), which appears to be the promoter of the upcoming digital world. 5G is defined by a single piece of cellular access technology or a combination of advanced access technologies. Rather, 5G is a true network assembler that provides consistent support for a slew of novel network topologies. Prior generations provide as a suitable starting point and give support for the security architecture for 5G security. Through authentication and cryptography techniques, many works have tackled the security issues in 3G and 4G networks in an effective manner. However, security of 5G networks while data transmission was not improved. The IoT aware Quadratic Discriminant Analysis (QDA) Node Authentication Method is proposed in order to achieve safe data communication in the 5G network. The suggested method performs feature selection using Quadratic Discriminant Analysis to identify relevant properties of mobile nodes such as trust value, residual energy level, and node cooperativeness. Simulation can be used to test the data packets, the number of mobile nodes in a security level, the computation overhead, and the authentication accuracy. The observed output clearly demonstrates that the Quadratic Discriminant Analysis Method significantly enhances the authentication accuracy of each node and the security level with less overhead than state-of-the-art-methods.

Keywords


Cite This Article

M. P. Haripriya and P. Venkadesh, "Feature selection based on iot aware qda node authentication in 5g networks," Intelligent Automation & Soft Computing, vol. 33, no.2, pp. 825–836, 2022.



cc 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.
  • 1164

    View

  • 754

    Download

  • 0

    Like

Share Link