DARMA: Defeating and Reconnaissance Manna-karma Attacks in 802.11 with Multiple Detections and Prevention

Baharudin, Norzaidi

doi:10.30534/ijatcse/2020/1391.32020

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Beacon Flood1

Karma-manna Attack1

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2023

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(2 citation statements)

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“…Experiments and frame density measurements conducted in [32] are summarized in Table 2. It shows the full set of Beacon frames that were collected in various locations.…”

Section: Beacon Floodmentioning

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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“…Experiments and frame density measurements conducted in [32] are summarized in Table 2. It shows the full set of Beacon frames that were collected in various locations.…”

Section: Beacon Floodmentioning

confidence: 99%

“…This detection rule is based on a mechanism referred to as DARMA, as proposed in paper [32]. The detection implementation described in the paper consists of two stages:…”

Section: Karma-manna Attackmentioning

confidence: 99%