Authentication of Underwater Acoustic Transmissions via Machine Learning Techniques

We consider the problem of discriminating a legitimate transmitter from an impersonating attacker in an underwater acoustic network under a physical layer security framework. In particular, we utilize features of the underwater acoustic channel such as the number of taps, the delay spread, and the received power. In the absence of a reliable statistical model of the underwater channel, we turn to ...