Learning secure modulation using complex neural networks

Hesham Mohammed; Dola Saha

doi:10.1117/12.2618811

31 May 2022 Learning secure modulation using complex neural networks

Hesham Mohammed, Dola Saha

Proceedings Volume 12097, Big Data IV: Learning, Analytics, and Applications; 1209709 (2022) https://doi.org/10.1117/12.2618811
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Growing interest in utilizing wireless spectrum makes spectrum access congested, competitive, and often contested making wireless signals vulnerable to various attacks. This compels us to design a secure waveform that solves encryption and modulation as a joint problem. We propose a novel end-to-end symmetric key encryption algorithm where the transmitter encodes the confidential data bits using a shared secret key to generate a secure waveform and the legitimate receiver decrypts the waveform to retrieve the transmitted bits. The trusted pairs are trained adversarially to learn secure data communication by introducing an adversarial NN, that helps to separate the mutual information between secret bits and secured waveform. Cooperative learning takes place between the trusted pair to defeat the adversary and learn encryption and modulation jointly. Complex neural networks are used to build encryption/decryption networks to improve the secrecy-reliability trade-off compared to prior works. Extensive simulated data set is used to train the trusted pair to learn secure data transmission. Our results demonstrate that the trusted pair succeeds in achieving secure data transmission over wireless links while the adversary can not decode or recognize the received waveform.

Conference Presentation

Citation Download Citation

Hesham Mohammed and Dola Saha "Learning secure modulation using complex neural networks", Proc. SPIE 12097, Big Data IV: Learning, Analytics, and Applications, 1209709 (31 May 2022); https://doi.org/10.1117/12.2618811

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