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Deep learning-assisted detection of PUE and jamming attacks in cognitive radio systems

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Date

2020

Author

Aygül, Mehmet Ali
Furqan, Haji Muhammad
Nazzal, Mahmoud
Arslan, Hüseyin

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Citation

Aygül, M. A., Furqan, H. M., Nazzal, M. ve Arslan, H. (2020). Deep learning-assisted detection of PUE and jamming attacks in cognitive radio systems. 92nd IEEE Vehicular Technology Conference (IEEE VTC-Fall). Virtual, Victoria, Canada, 18-16 November 2020. https://dx.doi.org/10.1109/VTC2020-Fall49728.2020.9348579

Abstract

Cognitive radio (CR)-based internet of things systems can be considered as an efficient solution for futuristic smart technologies. However, CRs are naturally vulnerable to two major security threats; primary user emulation (PUE) and jamming attacks. Machine learning has been recently applied to the detection of these attacks. Still, the need for feature extraction required by machine learning techniques restrains the full exploitation of raw data. To alleviate this need, this paper proposes one-dimensional deep learning as a framework for identifying such attacks. Simulations show the ability of the proposed algorithm to detect these attacks with high performance.

Source

92nd IEEE Vehicular Technology Conference (IEEE VTC-Fall)

Volume

2020

URI

https://dx.doi.org/10.1109/VTC2020-Fall49728.2020.9348579
https://hdl.handle.net/20.500.12511/6603

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  • Scopus İndeksli Yayınlar Koleksiyonu [5815]
  • WoS İndeksli Yayınlar Koleksiyonu [5982]



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