Growing interest in utilizing the wireless spectrum by heterogeneous devices compels us to rethink the physical layer security to protect the transmitted waveform from an eavesdropper. We propose an end-to-end symmetric key neural encryption and decryption algorithm with a modulation technique, which remains undeciphered by an eavesdropper, equipped with the same neural network and trained on the same dataset as the intended users. We solve encryption and modulation as a joint problem for which we map the bits to complex analog signals, without adhering to any particular encryption algorithm or modulation technique. We train to cooperatively learn encryption and decryption algorithms between our trusted pair of neural networks, while eavesdropper’s model is trained adversarially on the same data to minimize the error. We introduce a discrete activation layer with a defined gradient to combat noise in a lossy channel. Our results show that a trusted pair of users can exchange data bits in both clean and noisy channels, where a trained adversary cannot decipher the data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Learning Secured Modulation With Deep Adversarial Neural Networks


    Beteiligte:
    Mohammed, Hesham (Autor:in) / Saha, Dola (Autor:in)


    Erscheinungsdatum :

    2020-11-01


    Format / Umfang :

    2265791 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deep Learning with Chaotic Encryption based Secured Ethnicity Recognition

    Christy, C. / Arivalagan, S. / Sudhakar, P. | IEEE | 2019


    Crack Detection Based on Generative Adversarial Networks and Deep Learning

    Chen, Gongfa / Teng, Shuai / Lin, Mansheng et al. | Springer Verlag | 2022


    Deep Neural Network based Secured Control of Flying Vehicle in Urban Environment

    Zaidi, Adeel / Kazim, Muhammad / Zhang, Lixian et al. | IEEE | 2022



    Digital holography with deep learning and generative adversarial networks for automatic microplastics classification

    Zhu, Yanmin / Yeung, Chok Hang / Lam, Edmund Y. | British Library Conference Proceedings | 2020