DOI: 10.3390/electronics12153300 ISSN: 2079-9292

Anomaly Detection in 6G Networks Using Machine Learning Methods

Mamoon M. Saeed, Rashid A. Saeed, Maha Abdelhaq, Raed Alsaqour, Mohammad Kamrul Hasan, Rania A. Mokhtar
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Control and Systems Engineering

While the cloudification of networks with a micro-services-oriented design is a well-known feature of 5G, the 6G era of networks is closely related to intelligent network orchestration and management. Consequently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) have a big part to play in the 6G paradigm that is being imagined. Future end-to-end automation of networks requires proactive threat detection, the use of clever mitigation strategies, and confirmation that 6G networks will be self-sustaining. To strengthen and consolidate the role of AI in safeguarding 6G networks, this article explores how AI may be employed in 6G security. In order to achieve this, a novel anomaly detection system for 6G networks (AD6GNs) based on ensemble learning (EL) for communication networks was redeveloped in this study. The first stage in the EL-ADCN process is pre-processing. The second stage is the feature selection approach. It applies the reimplemented hybrid approach using a comparison of the ensemble learning and feature selection random forest algorithms (CFS-RF). NB2015, CIC_IDS2017, NSL KDD, and CICDDOS2019 are the three datasets, each given a reduced dimensionality, and the top subset characteristic for each is determined separately. Hybrid EL techniques are used in the third step to find intrusions. The average voting methodology is employed as an aggregation method, and two classifiers—support vector machines (SVM) and random forests (RF)—are modified to be used as EL algorithms for bagging and adaboosting, respectively. Testing the concept of the last step involves employing classification forms that are binary and multi-class. The best experimental results were obtained by applying 30, 35, 40, and 40 features of the reimplemented system to the three datasets: NSL_KDD, UNSW_NB2015, CIC_IDS2017, and CICDDOS2019. For the NSL_KDD dataset, the accuracy was 99.5% with a false alarm rate of 0.0038; the accuracy was 99.9% for the UNSW_NB2015 dataset with a false alarm rate of 0.0076; and the accuracy was 99.8% for the CIC_IDS2017 dataset with a false alarm rate of 0.0009. However, the accuracy was 99.95426% for the CICDDOS2019 dataset, with a false alarm rate of 0.00113.

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