iWEP: An Intelligent WLAN Early Warning Platform Using Edge Computing

Liu, Runhao; Wang, Weitao; Wang, Jiayao; Ou, Zhixin; Qiu, Haozhong; Wang, Benyu; Liu, Qiang

doi:10.1109/msn48538.2019.00079

Cited by 2 publications

(2 citation statements)

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“…However, the ladder network had lower efficacy in identifying 'injection' and 'impersonation' attacks, with an average decrease of 10% compared to other neural networks. In paper [5], the authors present a solution that combines rule-based and machine-learning methods for detecting security threats in wireless networks. The proposed architecture leverages edge computing to achieve a balance between high security and low latency.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we propose an enhanced WIDS that uses rule-based approaches supported by machine learning techniques to detect different networking threats. This system was inspired by the OpenWIPS-ng [4] project and papers like [5], but upon further investigation, it became apparent that they lacked sufficient functionalities we decided to fill these in. IDS systems are engineered to detect and notify administrators of any potential security risks to a wireless network.…”

Section: Introductionmentioning

confidence: 99%

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

Uszko,

Kasprzyk,

Natkaniec

et al. 2023

Electronics

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%