A Survey of Spoofer Detection Techniques via Radio Frequency Fingerprinting with Focus on the GNSS Pre-Correlation Sampled Data

Abstract

Radio frequency fingerprinting (RFF) methods are becoming more and more popular in the context of identifying genuine transmitters and distinguishing them from malicious or non-authorized transmitters, such as spoofers and jammers. RFF approaches have been studied to a moderate-to-great extent in the context of non-GNSS transmitters, such as WiFi, IoT, or cellular transmitters, but they have not yet been addressed much in the context of GNSS transmitters. In addition, the few RFF-related works in GNSS context are based on post-correlation or navigation data and no author has yet addressed the RFF problem in GNSS with pre-correlation data. Moreover, RFF methods in any of the three domains (pre-correlation, post-correlation, or navigation) are still hard to be found in the context of GNSS. The goal of this paper was two-fold: first, to provide a comprehensive survey of the RFF methods applicable in the GNSS context; and secondly, to propose a novel RFF methodology for spoofing detection, with a focus on GNSS pre-correlation data, but also applicable in a wider context. In order to support our proposed methodology, we qualitatively investigated the capability of different methods to be used in the context of pre-correlation sampled GNSS data, and we present a simulation-based example, under ideal noise conditions, of how the feature down selection can be done. We are also pointing out which of the transmitter features are likely to play the biggest roles in the RFF in GNSS, and which features are likely to fail in helping RFF-based spoofing detection.

A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges

“…The survey in [16] presents a short account of the RF fingerprint extraction and authentication methods with an emphasis on device authenticity -legal or illicit. Another survey in [17] reviews spoofer detection methods that leverages RF fingerprinting with special emphasis on Global Navigation Satellite System (GNSS) emitters. Although this work presents a broader scope in contrast to [15], [16], it lacks a thorough presentation of all aspects of RF signal intelligence.…”

Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

“…The first step in our analysis has been to test which feature or combinations of features give the most promising LOS detection results. For this scope, five features have been selected, based on prior literature studies as shown in Table 1 : Time correlation—this is the most straightforward feature and has been illustrated, for example in Figure 4 ; Fourier transform of the time correlation, as a representative of frequency-domain characteristics; Kurtosis; Skewness: typically, skewness is higher for NLOS channels than for LOS channels; Teager-Kaiser transform (TK)—this transform was selected based on our previous work on feature identification in a GNSS context [ 46 ]. …”

“…Teager-Kaiser transform (TK)—this transform was selected based on our previous work on feature identification in a GNSS context [ 46 ].…”

A Survey on the Use of Deep Learning Techniques for UAV Jamming and Deception

“…Zhang et al [10] A survey on various applications of DL techniques in mobile and wireless networking. Carrio et al [11] A review focusing on applications of DL techniques in various UAV-related applications.…”

“…Due to the popularity of machine learning and deep learning, several studies have also been published about communication signals or wireless networks [7][8][9], which are closely related to the topic of UAVs. Several publications are also focused on applications of these methods to UAV on-board systems, management of the UAV itself or UAV swarms [9][10][11]. Despite all the identified benefits of machine learning in these areas, insufficient research As can be seen from Table 1, a number of studies have been conducted in recent years across the areas of UAV neutralization, communication signals and applications of machine and deep learning methods.…”