Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANs

The purpose of this paper is to design and implement a complete system for monitoring and detecting attacks and anomalies in 5G wireless local area networks. Regrettably, the development of most open source systems has been stopped, making them unable to detect emerging forms of threats. The system...

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Vydáno v:Electronics (Basel) Ročník 12; číslo 11; s. 2355
Hlavní autoři: Uszko, Krzysztof, Kasprzyk, Maciej, Natkaniec, Marek, Chołda, Piotr
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 23.05.2023
Témata:
5G mobile communication
Accuracy
Anomalies
Connectivity
Data security
Efficiency
Intrusion detection systems
Local area networks
Machine learning
Methods
Modular systems
Network security
Safety and security measures
Wide area networks
Wireless local area networks (Computer networks)
Wireless networks
Poland
ISSN:2079-9292, 2079-9292
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Shrnutí:The purpose of this paper is to design and implement a complete system for monitoring and detecting attacks and anomalies in 5G wireless local area networks. Regrettably, the development of most open source systems has been stopped, making them unable to detect emerging forms of threats. The system provides a modular framework to create and add new detection rules as new attacks emerge. The system is based on packet analysis modules and rules and incorporates machine learning models to enhance its efficiency. The use of rule-based detection establishes a strong basis for the identification of recognized threats, whereas the additional implementation of machine learning models enables the detection of new and emerging attacks at an early stage. Therefore, the ultimate aim is to create a tool that constantly evolves by integrating novel attack detection techniques. The efficiency of the system is proven experimentally with accuracy levels up to 98.57% and precision as well as recall scores as high as 92%.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12112355