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.
|Title of host publication||2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - 2021|
|Event||2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021 - Tel Aviv, Israel|
Duration: 1 Nov 2021 → 3 Nov 2021
|Name||2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021|
|Conference||2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021|
|Period||1/11/21 → 3/11/21|
Bibliographical noteFunding 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 of Excellence (Law 232/2016).
© 2021 IEEE.
- 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