Authentication by Location Tracking in Underwater Acoustic Networks

TL;DR

This paper introduces a context-based underwater device authentication method that estimates and predicts device positions using neural networks and filters, enhancing security by verifying transmission authenticity through location consistency.

Contribution

It proposes a novel two-step authentication approach combining CNN-based position estimation and Kalman filter or RNN prediction for underwater acoustic networks.

Findings

Kalman filter-based prediction outperforms RNN in correlated motion scenarios.

The method effectively detects device movement and authenticates transmissions.

Location estimation accuracy improves with covariance matrix input.

Abstract

Physical layer message authentication in underwater acoustic networks (UWANs) leverages the characteristics of the underwater acoustic channel (UWAC) as a fingerprint of the transmitting device. However, as the device moves its UWAC changes, and the authentication mechanism must track such variations. In this paper, we propose a context-based authentication mechanism operating in two steps: first, we estimate the position of the underwater device, then we predict its future position based on the previously estimated ones. To check the authenticity of the transmission, we compare the estimated and the predicted position. The location is estimated using a convolutional neural network taking as input the sample covariance matrix of the estimated UWACs. The prediction uses either a Kalman filter or a recurrent neural network (RNN). The authentication check is performed on the squared error between the predicted and estimated positions. The solution based on the Kalman filter outperforms that built on the RNN when the device moves according to a correlated Gauss-Markov mobility model, which reproduces a typical underwater motion.