We propose a technique to authenticate received packets in underwater acoustic networks based on the physicallayer features of the underwater acoustic 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 is received, b) obtain a compressed feature representation through a neural network (NN), and c) transmit their representations to a central sink node that, using a NN, decides whether the packet has been transmitted by the legitimate node or by an impersonating attacker. Although the purpose of the system 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 still taking into account transmission rate limits among the nodes. We consider both global training, where all NNs are trained together, and local training, where each NN is trained individually. For the latter scenario, several alternatives for the NN structure and loss function were used for training.

Machine Learning-Based Distributed Authentication of UWAN Nodes with Limited Shared Information / Ardizzon, Francesco; Diamant, Roee; Casari, Paolo; Tomasin, Stefano. - (2022), pp. 1-5. (Intervento presentato al convegno 6th Underwater Communications and Networking Conference, UComms 2022 tenutosi a Lerici, Italy nel 30 August-1 September 2022) [10.1109/UComms56954.2022.9905689].

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

Diamant, Roee;Casari, Paolo;
2022-01-01

Abstract

We propose a technique to authenticate received packets in underwater acoustic networks based on the physicallayer features of the underwater acoustic 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 is received, b) obtain a compressed feature representation through a neural network (NN), and c) transmit their representations to a central sink node that, using a NN, decides whether the packet has been transmitted by the legitimate node or by an impersonating attacker. Although the purpose of the system 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 still taking into account transmission rate limits among the nodes. We consider both global training, where all NNs are trained together, and local training, where each NN is trained individually. For the latter scenario, several alternatives for the NN structure and loss function were used for training.
2022
Proc. UCOMMS
345 E 47TH ST, NEW YORK, NY 10017 USA
IEEE
978-1-6654-7461-0
Ardizzon, Francesco; Diamant, Roee; Casari, Paolo; Tomasin, Stefano
Machine Learning-Based Distributed Authentication of UWAN Nodes with Limited Shared Information / Ardizzon, Francesco; Diamant, Roee; Casari, Paolo; Tomasin, Stefano. - (2022), pp. 1-5. (Intervento presentato al convegno 6th Underwater Communications and Networking Conference, UComms 2022 tenutosi a Lerici, Italy nel 30 August-1 September 2022) [10.1109/UComms56954.2022.9905689].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/355881
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