Replay-Attack Countermeasures for Underwater Acoustic Networks

Campagnaro, Filippo; Tronchin, Davide; Signori, Alberto; Petroccia, Roberto; Pelekanakis, Konstantinos; Paglierani, Pietro; Alves, João; Zorzi, Michele

doi:10.1109/ieeeconf38699.2020.9389259

Cited by 5 publications

(1 citation statement)

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“…While some signaling and networking protocols may offer a first barrier against attacks on UWANs [4]- [7], a recent trend explores the fundamental characteristics of the underwater acoustic channel (UWAC) to secure underwater communications [8]. Among the first examples of underwater physical layer security, Kulhandjian et al exploit jamming to disturb unwanted receptions at an eavesdropper, while still allowing communications between a pair of legitimate transceivers [9].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Authentication of Underwater Acoustic Transmissions via Machine Learning Techniques

Bragagnolo

Ardizzon

Laurenti

et al. 2021

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

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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.

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Section: Introduction and Related Workmentioning

confidence: 99%