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RF Jamming Dataset: A Wireless Spectral Scan Approach for Malicious Interference Detection
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  • Abubakar Sani Ali ,
  • Govind Singh ,
  • Willian T. Lunardi ,
  • Lina Bariah ,
  • Michael Baddeley ,
  • Martin Andreoni ,
  • Jean-Pierre Giacalone ,
  • Sami Muhaidat
Abubakar Sani Ali
Khalifa Univerisy

Corresponding Author:[email protected]

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Govind Singh
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Willian T. Lunardi
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Lina Bariah
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Michael Baddeley
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Martin Andreoni
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Jean-Pierre Giacalone
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Sami Muhaidat
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Abstract

Future wireless networks must incorporate awareness, adaptability, and intelligence as fundamental building elements in order to meet the wide range of requirements of the next-generation communication systems. Wireless sensing techniques can be used to gather awareness from the radio signals present in the surroundings. However, threats from hostile attackers, such as jamming, eavesdropping, and manipulation, are also present along with this. This paper describes in detail an RF-jamming detection testbed and provides experimentally measured data. The RF jamming detection testbed, in particular, makes use of the spectral scan capability of wireless network interfaces and JamRF, a jamming toolkit. In order to facilitate future progress in the experimentation of jamming detection and avoidance systems, we explain the methodology used for the development of our testbed and discuss the reasoning behind the choices made throughout its evolution. Furthermore, we provide various types of measurement data obtained with the testbed. The data set is expected to facilitate and promote the experimental evaluation of jamming detection, jamming avoidance, and anti-jamming schemes developed by the wireless security community. As an illustration, we trained five different machine learning algorithms and got results with an overall jamming detection accuracy of more than 90% for all algorithms.