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Intrusion Detection in 5G Cellular Network Using Machine Learning

Ishtiaque Mahmood1, Tahir Alyas2, Sagheer Abbas3, Tariq Shahzad4, Qaiser Abbas5,6, Khmaies Ouahada7,*

1 Knowledge Unit of Systems and Technology, UMT Sialkot Campus, Sialkot, 51040, Pakistan
2 Department of Computer Science, Lahore Garrison University, Lahore, 54000, Pakistan
3 Faculty of Computer Science, National College of Business Administration and Economics, Lahore, 54660, Pakistan
4 Department of Electrical and Computer Engineering, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan
5 Faculty of Computer and Information Systems Islamic University of Madinah, Madinah, 42351, Saudi Arabia
6 Department of Computer Science & IT, University of Sargodha, Sargodha, 40100, Pakistan
7 Department of Electrical and Electronic Engineering Science, University of Johannesburg, P.O Box 524, Auckland Park, Johannesburg, 2006, South Africa

* Corresponding Author: Khmaies Ouahada. Email: email

Computer Systems Science and Engineering 2023, 47(2), 2439-2453. https://doi.org/10.32604/csse.2023.033842

Abstract

Attacks on fully integrated servers, apps, and communication networks via the Internet of Things (IoT) are growing exponentially. Sensitive devices’ effectiveness harms end users, increases cyber threats and identity theft, raises costs, and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended periods. Attacks on Internet of Things interfaces must be closely monitored in real time for effective safety and security. Following the 1, 2, 3, and 4G cellular networks, the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular networks. Even to this day, experts are working on the evolution’s sixth generation (6G). It offers amazing capabilities for connecting everything, including gadgets and machines, with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 GHz. It gives you the most recent information. Many countries have already established this technology within their border. Security is the most crucial aspect of using a 5G network. Because of the absence of study and network deployment, new technology first introduces new gaps for attackers and hackers. Internet Protocol(IP) attacks and intrusion will become more prevalent in this system. An efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this research. This research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network, such as intruder hackers/attackers. After applying different machine learning algorithms, obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12% on test data and 92.13% on train data with 92% precision.

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Cite This Article

APA Style
Mahmood, I., Alyas, T., Abbas, S., Shahzad, T., Abbas, Q. et al. (2023). Intrusion detection in 5G cellular network using machine learning. Computer Systems Science and Engineering, 47(2), 2439-2453. https://doi.org/10.32604/csse.2023.033842
Vancouver Style
Mahmood I, Alyas T, Abbas S, Shahzad T, Abbas Q, Ouahada K. Intrusion detection in 5G cellular network using machine learning. Comput Syst Sci Eng. 2023;47(2):2439-2453 https://doi.org/10.32604/csse.2023.033842
IEEE Style
I. Mahmood, T. Alyas, S. Abbas, T. Shahzad, Q. Abbas, and K. Ouahada "Intrusion Detection in 5G Cellular Network Using Machine Learning," Comput. Syst. Sci. Eng., vol. 47, no. 2, pp. 2439-2453. 2023. https://doi.org/10.32604/csse.2023.033842



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.
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