As underwater acoustic communication technologies become mature and underwater networks evolve into reliable solutions for data communications, authenticating transmitted data turns from an option to a necessity. In this paper, we explore physical-layer authentication for an underwater acoustic networks with mobile devices. We choose the power-weighted average of the channel taps' arrival delay as the main authentication feature. We then develop a Kalman filter approach to track the evolution of this feature in the presence of mobility. The filter computes an innovation metric for each new transmission, which is processed to determine if a signal originates from a legitimate network node. Simulation results obtained from a dataset generated with Bellhop show that our authentication mechanism successfully distinguishes between legitimate and impersonating transmitters. Moreover, we show that linearly combining innovation readings from multiple sensors yields a good low-complexity classifier, and assess the impact of the transmitter speed on the authentication performance.
Physical Layer Authentication in Underwater Acoustic Networks with Mobile Devices / Casari, Paolo; Ardizzon, Francesco; Tomasin, Stefano. - ELETTRONICO. - (2022). (Intervento presentato al convegno WUWNet tenutosi a Boston nel 14th - 16th November 2022).
Physical Layer Authentication in Underwater Acoustic Networks with Mobile Devices
Paolo Casari;
2022-01-01
Abstract
As underwater acoustic communication technologies become mature and underwater networks evolve into reliable solutions for data communications, authenticating transmitted data turns from an option to a necessity. In this paper, we explore physical-layer authentication for an underwater acoustic networks with mobile devices. We choose the power-weighted average of the channel taps' arrival delay as the main authentication feature. We then develop a Kalman filter approach to track the evolution of this feature in the presence of mobility. The filter computes an innovation metric for each new transmission, which is processed to determine if a signal originates from a legitimate network node. Simulation results obtained from a dataset generated with Bellhop show that our authentication mechanism successfully distinguishes between legitimate and impersonating transmitters. Moreover, we show that linearly combining innovation readings from multiple sensors yields a good low-complexity classifier, and assess the impact of the transmitter speed on the authentication performance.File | Dimensione | Formato | |
---|---|---|---|
WUWNet2022_Authentication_Mobility_UW.pdf
accesso aperto
Tipologia:
Versione editoriale (Publisher’s layout)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
1.29 MB
Formato
Adobe PDF
|
1.29 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione