Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014 2014
DOI: 10.1109/wowmom.2014.6918964
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Machine learning-based jamming detection for IEEE 802.11: Design and experimental evaluation

Abstract: Abstract-Jamming is a well-known reliability threat for mass-market wireless networks. With the rise of safety-critical applications this is likely to become a constraining issue in the future. Thus, the design of accurate jamming detection algorithms becomes important to react to ongoing jamming attacks. With respect to experimental work, jamming detection has been mainly studied for sensor networks. However, many safety-critical applications are also likely to run over 802.11-based networks where the propose… Show more

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Cited by 65 publications
(61 citation statements)
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“…In particular, we use a framework based on Crawler that provides 802.11 devices with capabilities for detecting the presence of jamming attacks in a distributed and reliable manner [10]. We enhanced the framework to support remote automation, configuration, and monitoring.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…In particular, we use a framework based on Crawler that provides 802.11 devices with capabilities for detecting the presence of jamming attacks in a distributed and reliable manner [10]. We enhanced the framework to support remote automation, configuration, and monitoring.…”
Section: Discussionmentioning
confidence: 99%
“…1 The details of this specific optimization are presented in [10]. Realize cross-layer optimization Figure 2: The addChainsApp allows to remotely configure optimizations.…”
Section: Remote Configurationmentioning
confidence: 99%
“…Several techniques have been proposed to detect channel jamming [14][15][16][17][18][19]. However, the main issue in this approach is to derive a new channel number that is common to both APs and users.…”
Section: Related Workmentioning
confidence: 99%
“…In this system, they implemented multiple ML based classifiers to enable a better performance and their experiments showed an accuracy exceeding 96%. Oscar Puñal et al presented an ML-based jamming detection for WLAN [15], using multiple metrics, including channel metrics, performance metrics ad signal metrics to collect data and multiple ML algorithm to recognize if there exists a jamming. Their research showed that supervised approaches exhibit similar performance while unsupervised learning ones is not suited for jamming detection.…”
Section: Machine Learning: An Intelligent Futurementioning
confidence: 99%