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We propose a technique to authenticate received packets in underwateracoustic networks based on the physical layer features of the underwateracoustic channel (UWAC). Several sensors a) locally estimate features (e.g.,the number of taps or the delay spread) of the UWAC over which the packet isreceived, b) obtain a compressed feature representation through a neuralnetwork (NN), and c) transmit their representations to a central sink nodethat, using a NN, decides whether the packet has been transmitted by thelegitimate node or by an impersonating attacker. Although the purpose of thesystem is to make a binary decision as to whether a packet is authentic or not,we show the importance of having a rich set of compressed features, while stilltaking into account transmission rate limits among the nodes. We consider bothglobal training, where all NNs are trained together, and local training, whereeach NN is trained individually. For the latter scenario, several alternativesfor the NN structure and loss function were used for training.

Machine Learning-Based Distributed Authentication of UWAN Nodes with Limited Shared Information