arXiv subjects

Francesco Formaggio

Publications and source records attributed to Francesco Formaggio.

3 recordsLinked to original sources

arXiv2019

Machine Learning For In-Region Location Verification In Wireless Networks

In-region location verification (IRLV) aims at verifying whether a user is inside a region of interest (ROI). In wireless networks, IRLV can exploit the features of the channel between the user and a set of trusted access points. In practice, the channel feature statistics is not available and we resort to machine learning (ML) solutions for IRLV. We first show that solutions based on either neural networks (NNs) or support vector machines (SVMs) and typical loss functions are Neyman-Pearson (N-P)-optimal at learning convergence for sufficiently complex learning machines and large training datasets . Indeed, for finite training, ML solutions are more accurate than the N-P test based on estimated channel statistics. Then, as estimating channel features outside the ROI may be difficult, we consider one-class classifiers, namely auto-encoders NNs and one-class SVMs, which however are not equivalent to the generalized likelihood ratio test (GLRT), typically replacing the N-P test in the one-class problem. Numerical results support the results in realistic wireless networks, with channel models including path-loss, shadowing, and fading.

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arXiv2019

Location-Verification and Network Planning via Machine Learning Approaches

In-region location verification (IRLV) in wireless networks is the problem of deciding if user equipment (UE) is transmitting from inside or outside a specific physical region (e.g., a safe room). The decision process exploits the features of the channel between the UE and a set of network access points (APs). We propose a solution based on machine learning (ML) implemented by a neural network (NN) trained with the channel features (in particular, noisy attenuation values) collected by the APs for various positions both inside and outside the specific region. The output is a decision on the UE position (inside or outside the region). By seeing IRLV as an hypothesis testing problem, we address the optimal positioning of the APs for minimizing either the area under the curve (AUC) of the receiver operating characteristic (ROC) or the cross entropy (CE) between the NN output and ground truth (available during the training). In order to solve the minimization problem we propose a twostage particle swarm optimization (PSO) algorithm. We show that for a long training and a NN with enough neurons the proposed solution achieves the performance of the Neyman-Pearson (N-P) lemma.

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arXiv2018

Authentication of Satellite Navigation Signals by Wiretap Coding and Artificial Noise

In order to combat the spoofing of global navigation satellite system (GNSS) signals we propose a novel approach for satellite signal authentication based on information-theoretic security. In particular we superimpose to the navigation signal an authentication signal containing a secret message corrupted by artificial noise (AN), still transmitted by the satellite. We impose the following properties: a) the authentication signal is synchronous with the navigation signal, b) the authentication signal is orthogonal to the navigation signal and c) the secret message is undecodable by the attacker due to the presence of the AN. The legitimate receiver synchronizes with the navigation signal and stores the samples of the authentication signal with the same synchronization. After the transmission of the authentication signal, through a separate public asynchronous authenticated channel (e.g., a secure Internet connection) additional information is made public allowing the receiver to a) decode the secret message, thus overcoming the effects of AN, and b) verify the secret message. We assess the performance of the proposed scheme by the analysis of both the secrecy capacity of the authentication message and the attack success probability, under various attack scenarios. A comparison with existing approaches shows the effectiveness of the proposed scheme.

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