Authentication of Underwater Acoustic Transmissions via Machine Learning Techniques

L. Bragagnolo, F. Ardizzon, N. Laurenti, P. Casari, R. Diamant, S. Tomasin

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

Abstract

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 a machine learning technique to extract the feature statistics and utilize them to distinguish between legitimate and fake transmissions. Numerical results show how, using only four channel features as input of either a neural network or an autoencoder, we achieve a good trade off between false alarm and detection rates. Moreover, cooperative techniques fusing soft decision statistics from multiple trusted nodes further outperform the discrimination capability of each separate node. Data from a sea trial carried out in Israeli eastern Mediterranean waters demonstrate the applicability of our approach.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages255-260
Number of pages6
ISBN (Electronic)9780738146720
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021 - Tel Aviv, Israel
Duration: 1 Nov 20213 Nov 2021

Publication series

Name2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021

Conference

Conference2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021
Country/TerritoryIsrael
CityTel Aviv
Period1/11/213/11/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Authentication
  • Physical layer security
  • Underwater acoustic channel

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing
  • Electrical and Electronic Engineering
  • Instrumentation

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