Adversarial Learning for Cross Layer Security

Spectrum access in the next generation wireless networks will be congested, competitive, and vulnerable to malicious intents of strong adversaries. This compels us to rethink wireless security for a cross-layer solution addressing it as a joint problem for encryption and modulation. We propose a novel neural network generated cross-layer security algorithm where the trusted transmitter encodes a secret message using a shared secret key to generate a secured waveform. This encrypted waveform remains undeciphered by the adversary while the intended receiver can recover the secret. Cooperative learning is introduced to enable our trusted pair to defeat the adversary and learn the encryption and modulation jointly. The model can encode any modulation order and improves both reliability and secrecy capacity compared to prior work. Our results demonstrate that the trusted pair succeeds in achieving secure data transmission while the adversary can not decipher the received cipher data.