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

Francesco Ardizzon, Roee Diamant, Paolo Casari, Stefano Tomasin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2022 6th Underwater Communications and Networking Conference, UComms 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665474610
DOIs
StatePublished - 2022
Event6th Underwater Communications and Networking Conference, UComms 2022 - Lerici, Italy
Duration: 30 Aug 20221 Sep 2022

Publication series

Name2022 6th Underwater Communications and Networking Conference, UComms 2022

Conference

Conference6th Underwater Communications and Networking Conference, UComms 2022
Country/TerritoryItaly
CityLerici
Period30/08/221/09/22

Bibliographical note

Funding Information:
This work was sponsored in part by the NATO Science for Peace and Security Programme under grant no. G5884 (SAFE-UComm), and by MIUR (Italian Ministry of Education) under the initiative Departments o f Excellence (Law 232/2016).

Publisher Copyright:
© 2022 IEEE.

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Oceanography
  • Acoustics and Ultrasonics

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