Attacks detection in 6G wireless networks using machine learning

Saeed, Mamoon M. and Saeed, Rashid A. and Gaid, Abdulguddoos S. A. and Mokhtar, Rania A. and Khalifa, Othman Omran and Ahmed, Zeinab E. (2023) Attacks detection in 6G wireless networks using machine learning. In: 2023 9th International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, Malaysia.

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Abstract

Unlike the fifth generation (5G), which is well recognized for network cloudification with micro-service-oriented design, the sixth generation (6G) of networks is directly tied to intelligent network orchestration and management. The Attacks Detection in 6G (AD6Gs) wireless networks created by this research uses a Machine Learning (ML) algorithm. The pre-processing stage of the ML-AD6Gs process is the initial step. The second stage involves the feature selection approach. Correlation Feature Selection algorithm (CFS) is used to implement the suggested hybrid strategy. It selects the best subset feature and reduces dimensionality for each independent analyses of the dataset CICDDOS2019. The voting average method is used as an aggregation step, and two classifiers—Random Forest (RF) and Support Vector Machine (SVM)—are modified to be used as ML Algorithms. The proposed method shown an outperformed the existing classification method. The accuracy was 99.9%% for CICDDOS2019 dataset with a false alarm rate of 0.00102

Item Type:	Proceeding Paper (Plenary Papers)
Uncontrolled Keywords:	Machine learning algorithm , correlation feature selection , random forest , support-vector machine , 5G , 6G security , Al , and ML
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr Othman O. Khalifa
Date Deposited:	04 Oct 2023 09:19
Last Modified:	02 Feb 2024 16:21
URI:	http://irep.iium.edu.my/id/eprint/107252

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