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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/11/2355 |
| _version_ | 1797597721280905216 |
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| author | Krzysztof Uszko Maciej Kasprzyk Marek Natkaniec Piotr Chołda |
| author_facet | Krzysztof Uszko Maciej Kasprzyk Marek Natkaniec Piotr Chołda |
| author_sort | Krzysztof Uszko |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-11T03:09:29Z |
| format | Article |
| id | doaj.art-568393775ae04ec9813edb7c7ebd29ef |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T03:09:29Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-568393775ae04ec9813edb7c7ebd29ef2023-11-18T07:43:48ZengMDPI AGElectronics2079-92922023-05-011211235510.3390/electronics12112355Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANsKrzysztof Uszko0Maciej Kasprzyk1Marek Natkaniec2Piotr Chołda3Institute of Telecommunications, AGH University of Krakow, 30-059 Kraków, PolandInstitute of Telecommunications, AGH University of Krakow, 30-059 Kraków, PolandInstitute of Telecommunications, AGH University of Krakow, 30-059 Kraków, PolandInstitute of Telecommunications, AGH University of Krakow, 30-059 Kraków, PolandThe 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%.https://www.mdpi.com/2079-9292/12/11/23555G Wi-Fi securityMAC layer threatsnetwork traffic analysisthreat detectionmachine learning |
| spellingShingle | Krzysztof Uszko Maciej Kasprzyk Marek Natkaniec Piotr Chołda Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANs Electronics 5G Wi-Fi security MAC layer threats network traffic analysis threat detection machine learning |
| title | Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANs |
| title_full | Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANs |
| title_fullStr | Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANs |
| title_full_unstemmed | Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANs |
| title_short | Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANs |
| title_sort | rule based system with machine learning support for detecting anomalies in 5g wlans |
| topic | 5G Wi-Fi security MAC layer threats network traffic analysis threat detection machine learning |
| url | https://www.mdpi.com/2079-9292/12/11/2355 |
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