@Article{csse.2023.033842,
AUTHOR = {Ishtiaque Mahmood, Tahir Alyas, Sagheer Abbas, Tariq Shahzad, Qaiser Abbas, Khmaies Ouahada},
TITLE = {Intrusion Detection in 5G Cellular Network Using Machine Learning},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {47},
YEAR = {2023},
NUMBER = {2},
PAGES = {2439--2453},
URL = {http://www.techscience.com/csse/v47n2/53682},
ISSN = {},
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.},
DOI = {10.32604/csse.2023.033842}
}