Authentication by Location Tracking in Underwater Acoustic Networks

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.

Challenge-Response to Authenticate Drone Communications: A Game Theoretic Approach

In this paper, we propose a novel strategy for physical layer authentications based on the challenge-response concept for a transmitting drone (Alice). In a preliminary training phase, Alice moves over several positions, and Bob (either a drone or a ground device) estimates the Alice-Bob channel gains. Then Alice transmits its message from different random positions (challenge) and Bob, upon receiving the messages, authenticates the sender via a log-likelihood test on the estimated channel gains (response). In turn, the intruder Trudy selects random positions on which she transmits messages on behalf of Alice to Bob. In this paper, we design the probability mass distribution of Alice's challenge positions and the Trudy response positions by modeling the problem as a zero-sum game between Bob and Trudy, where the payoff of Trudy is the missed detection probability. Moreover, we propose three different approaches that minimize the energy spent by Alice without sacrificing security, which differ in computational complexity and resulting energy consumption. Finally, we test the proposed technique via numerical simulations, which include a realistic model of both Alice-Bob and Trudy-Bob fading channels, affected by shadowing.

Performance Limits for Signals of Opportunity-Based Navigation

This paper investigates the potential of non-terrestrial and terrestrial signals of opportunity (SOOP) for navigation applications. Non-terrestrial SOOP analysis employs modified Cram\`er-Rao lower bound (MCRLB) to establish a relationship between SOOP characteristics and the accuracy of ranging information. This approach evaluates hybrid navigation module performance without direct signal simulation. The MCRLB is computed for ranging accuracy, considering factors like propagation delay, frequency offset, phase offset, and angle-of-arrival (AOA), across diverse non-terrestrial SOOP candidates. Additionally, Geometric Dilution of Precision (GDOP) and low earth orbit (LEO) SOOP availability are assessed. Validation involves comparing MCRLB predictions with actual ranging measurements obtained in a realistic simulated scenario. Furthermore, a qualitative evaluation examines terrestrial SOOP, considering signal availability, accuracy attainability, and infrastructure demands.

One-Class Classification as GLRT for Jamming Detection in Private 5G Networks

5G mobile networks are vulnerable to jamming attacks that may jeopardize valuable applications such as industry automation. In this paper, we propose to analyze radio signals with a dedicated device to detect jamming attacks. We pursue a learning approach, with the detector being a CNN implementing a GLRT. To this end, the CNN is trained as a two-class classifier using two datasets: one of real legitimate signals and another generated artificially so that the resulting classifier implements the GLRT. The artificial dataset is generated mimicking different types of jamming signals. We evaluate the performance of this detector using experimental data obtained from a private 5G network and several jamming signals, showing the technique's effectiveness in detecting the attacks.

Energy-Based Optimization of Physical-Layer Challenge-Response Authentication with Drones

Drones are expected to be used for many tasks in the future and require secure communication protocols. In this work, we propose a novel physical layer authentication (PLA)-based challenge-response (CR) protocol in which a drone Bob authenticates the sender (either on the ground or air) by exploiting his prior knowledge of the wireless channel statistic (fading, path loss, and shadowing). In particular, Bob will move to a set of positions in the space, and by estimating the attenuations of the received signals he will authenticate the sender. We take into account the energy consumption in the design and provide three solutions: a purely greedy solution (PG), an optimal Bellman iterative solution (BI), and a heuristic solution based on the evaluation of the standard deviation of the attenuations in the space. Finally, we demonstrate the effectiveness of our approach through numerical simulations.

Secret-Key-Agreement Advantage Distillation With Quantization Correction

We propose a novel advantage distillation strategy for physical layer-based secret-key-agreement (SKA). We consider a scenario where Alice and Bob aim at extracting a common bit sequence, which should remain secret to Eve, by quantizing a random number obtained from measurements at their communication channel. We propose an asymmetric advantage distillation protocol with two novel features: i) Alice quantizes her measurement and sends partial information on it over an authenticated public side channel, and ii) Bob quantizes his measurement by exploiting the partial information. The partial information on the position of the measurement in the quantization interval and its sharing allows Bob to obtain a quantized value closer to that of Alice. Both strategies increase the lower bound of the secret key rate.

On the Limits of Cross-Authentication Checks for GNSS Signals

Global navigation satellite systems (GNSSs) are implementing security mechanisms: examples are Galileo open service navigation message authentication (OS-NMA) and GPS chips-message robust authentication (CHIMERA). Each of these mechanisms operates in a single band. However, nowadays, even commercial GNSS receivers typically compute the position, velocity, and time (PVT) solution using multiple constellations and signals from multiple bands at once, significantly improving both accuracy and availability. Hence, cross-authentication checks have been proposed, based on the PVT obtained from the mixture of authenticated and non-authenticated signals. In this paper, first, we formalize the models for the cross-authentication checks. Next, we describe, for each check, a spoofing attack to generate a fake signal leading the victim to a target PVT without notice. We analytically relate the degrees of the freedom of the attacker in manipulating the victim's solution to both the employed security checks and the number of open signals that can be tampered with by the attacker. We test the performance of the considered attack strategies on an experimental dataset. Lastly, we show the limits of the PVT-based GNSS cross-authentication checks, where both authenticated and non-authenticated signals are used.

On the Optimal Spoofing Attack and Countermeasure in Satellite Navigation Systems

The threat of signal spoofing attacks against GNSS has grown in recent years and has motivated the study of anti-spoofing techniques. However, defense methods have been designed only against specific attacks. This paper introduces a general model of the spoofing attack framework in GNSS, from which optimal attack and defense strategies are derived. We consider a scenario with a legitimate receiver (Bob) testing if the received signals come from multiple legitimate space vehicles (Alice) or from an attack device (Eve). We first derive the optimal attack strategy against a Gaussian transmission from Alice, by minimizing an outer bound on the achievable error probability region of the spoofing detection test. Then, framing the spoofing and its detection as an adversarial game, we show that the Gaussian transmission and the corresponding optimal attack constitute a Nash equilibrium. Lastly, we consider the case of practical modulation schemes for Alice and derive the generalized likelihood ratio test. Numerical results validate the analytical derivations and show that the bound on the achievable error region is representative of the actual performance.

Generalized Likelihood Ratio Test With One-Class Classifiers

One-class classification (OCC) is the problem of deciding whether an observed sample belongs to a target class or not. We consider the problem of learning an OCC model when the dataset available at the learning stage contains only samples from the target class. We aim at obtaining a classifier that performs as the generalized likelihood ratio test (GLRT), which is a well-known and provably optimal (under specific assumptions) classifier when the statistic of the target class is available. To this end, we consider both the multilayer perceptron neural network (NN) and the support vector machine (SVM) models. They are trained as two-class classifiers using an artificial dataset for the alternative class, obtained by generating random samples, uniformly over the domain of the target-class dataset. We prove that, under suitable assumptions, the models converge (with a large dataset) to the GLRT. Moreover, we show that the one-class least squares SVM (OCLSSVM) at convergence performs as the GLRT, with a suitable transformation function. Lastly, we compare the obtained solutions with the autoencoder (AE) classifier, which does not in general provide the GLRT

Machine Learning-Based Distributed Authentication of UWAN Nodes with Limited Shared Information

We propose a technique to authenticate received packets in underwater acoustic networks based on the physical layer features of the underwater acoustic channel (UWAC). Several sensors a) locally estimate features (e.g., the number of taps or the delay spread) of the UWAC over which the packet is received, b) obtain a compressed feature representation through a neural network (NN), and c) transmit their representations to a central sink node that, using a NN, decides whether the packet has been transmitted by the legitimate node or by an impersonating attacker. Although the purpose of the system is to make a binary decision as to whether a packet is authentic or not, we show the importance of having a rich set of compressed features, while still taking into account transmission rate limits among the nodes. We consider both global training, where all NNs are trained together, and local training, where each NN is trained individually. For the latter scenario, several alternatives for the NN structure and loss function were used for training.