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.


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    Title :

    Deep Learning-Assisted Detection of PUE and Jamming Attacks in Cognitive Radio Systems


    Contributors:


    Publication date :

    2020-11-01


    Size :

    1441875 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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