ML-Based Advantage Distillation for Key Agreement in Underwater Acoustic Channels

Underwater networks have become an important topic of research due to their potential impact on several military and civil applications. To secure these communications, many protocols require the users to have symmetric secret keys. To distribute the keys, we can resort to physical layer key generation schemes, where the keys are generated by each user from the channel itself, by exploiting the environment as a source of randomness. We focus on the advantage distillation step of the secret key agreement (SKA) procedure, where the users extract the bit sequence from the channel observations. We propose an adversarial autoencoder (AAE) model for advantage distillation, which has multiple tasks: it enhances the reciprocity between legitimated channel features, decreases the reciprocity to the attacker channel features, and provides uniformly distributed bit sequences. The proposed approach is adaptive and directly derived from the training dataset, thus it does not require the a priori knowledge of the channel statistics. The solution is tested using channel observations collected from a sea experiment and compared to existing advantage distillation solutions, showing a significant improvement over the state of the art.