Open Access
ARTICLE
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:
Computer Systems Science and Engineering 2023, 47(2), 2439-2453. https://doi.org/10.32604/csse.2023.033842
Received 29 June 2022; Accepted 26 October 2022; Issue published 28 July 2023
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.
Keywords
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. Comp 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," Comp. Syst. Sci. Eng. , vol. 47, no. 2, pp. 2439-2453. 2023. https://doi.org/10.32604/csse.2023.033842