Spoofing, anti-spoofing, jamming and anti-jamming technologies have become an important research topic within the GNSS discipline. While many GNSS receivers leave a large space for signal dynamics, enough power space is left for the GNSS signals to be spoofed and/or jammed. The goal of spoofing is to provide the receiver with a misleading signal, fooling the receiver into using fake signals in the extra space for positioning calculations. The receiver will then generate a false position, thus misleading the navigator. The goal of jamming is to add noise to the satellite signal which leads to fooling the receiver into using “signals plus noise” for positioning calculations. This article discusses the approach to anti-jamming based on the shielding of antennas from the signal jammer.
The idea of C/A codes GPS/GNSS Spoofing (Substitution), or the ability to mislead a satellite navigation receiver into establishing a position or time fix which is incorrect, has been gaining attention as spoofing has become more sophisticated. Various techniques have been proposed to detect if a receiver is being spoofed – with varying degrees of success and computational complexity. If the jammer signals are sufficiently plausible then the GNSS receiver may not realize it has been duped. There are various means of detecting spoofing activity and hence providing effective mitigation methods. In this paper, a novel signal processing method applicable to a single antenna handset receiver for spoofing detection has been described. Mathematical models and algorithms have been developed to solve the problems of satellite navigation safety. What has been considered in the paper is a spoofing detection algorithm based on the analysis of a civil satellite signal generated by mobile C/A GPS/GNSS single-antenna receivers. The work has also served to refine the civilian spoofing threat assessment by demonstrating the challenges involved in mounting a spoofing attack.
Spoofing, anti-spoofing, jamming, and anti-jamming algorithms have become an important research topic within the Global Navigation Satellite System (GNSS) discipline. While many GNSS receivers leave large space for signal dynamics, enough power space is left for the GNSS signals to be spoofed. GNSS signal power on the earth’s surface is around 160 dBW. The goal of spoofing is to provide the receiver with a slightly more powerful misleading signal, stronger than the original GNSS signal, fooling the receiver into using fake signals for positioning calculations. The receiver will generate a misleading position of the navigator. Practical spoofing that provides misleading navigation results of the receiver is difficult to conduct due to the signal infrastructure. Using trivial anti-spoofing algorithms in GNSS receivers, spoofing attacks can be easily detected. The article discusses the vulnerability of unmanned vehicles and provides an approach to anti-spoofing based on measuring distance between two antennas.
Many civil GNSS (Global Navigation Satellite System) applications need secure, assured information for asset tracking, fleet management and the like. But there is also a growing demand for geosecurity location-based services. Unfortunately, GNSS is vulnerable to malicious intrusion and spoofing. How can users be sure that the information they receive is authentic? Spoofing is the transmission of matched-GNSS-signal-structure interference in an attempt to commandeer the tracking loops of a victim receiver and thereby manipulate the receiver’s timing or navigation solution. A spoofer can transmit its counterfeit signals from a stand-off distance of several hundred meters, or it can be co-located with its victim. Spoofing attacks can be classified as simple, intermediate, or sophisticated in terms of their effectiveness and subtlety. In an intermediate spoofing attack, a spoofer synchronizes its counterfeit signals with the authentic GNSS signals, so they are code-phase-aligned at the target receiver. In this paper, authors consider the antispoofing algorithms based on finding statistical anomalies in the basic parameters of the satellite signals. At the stage of learning, the system of antispoofing explores the statistical properties of signals and at the phase of spoofing detection, the system used thresholds characteristics of statistical anomalies. The excess of the threshold characteristics provides a basis for probabilistic decision on the presence of spoofing.
PL
Wiele cywilnych zastosowań GNSS (Globalnych Nawigacyjnych Systemów Satelitarnych) wymaga pewności, że informacje dotyczące śledzenia zasobów, zarządzania flotą itp. nie są sfałszowane. Na uwagę zasługuje także rosnący popyt na geobezpieczeństwo bazujące na usługach lokalizacji. Niestety GNSS jest podatny na preparowanie i modyfikowanie pakietów danych. Powstaje pytanie: jak użytkownicy mogą być pewni, że informacja, którą otrzymują jest autentyczna? Spoofing (ang. spoof – naciąganie, szachrajstwo) jest ingerencją w strukturę transmisji GNSS w celu modyfikacji pętli trasy odbiornika poszkodowanego, skutkiem czego jest manipulacja czasem na odbiorniku lub urządzeniem nawigacyjnym. Osoba podszywająca się może transmitować podrobiony sygnał z ukrycia w odległości do kilkuset metrów lub być współpołożona z jego ofiarą. Ataki spoofingu można zaklasyfikować jako proste, pośrednie i zaawansowane pod względem ich subtelności i efektywności. W ataku pośrednim osoba podszywająca się synchronizuje swój fałszywy sygnał z autentycznym sygnałem GNNS w taki sposób, iż następuje wyrównanie kodu–fazy dla odbiornika sygnału. W artykule przedstawiono algorytmy antyspoofingu, bazujące na znajdowaniu statystycznych anomalii w podstawowych parametrach sygnału satelitarnego. W trakcie funkcjonowania system antyspoofing bada statystyczne własności sygnałów i na etapie wykrycia spoofingu wykorzystuje charakterystyki progu anomalii. Nadmiar cech progowych stanowi podstawę do wykrycia spoofingu.
Current physical biometric verification systems are hampered by a major vulnerability: spoofing. Keeping biological information, such as the face, fingerprints, and irises, concealed from others in daily life is difficult, and therefore theft and counterfeit of exposed biological information can be relatively easily accomplished by first capturing an individual's targeted information as an image and then using the data to reproduce a model. Here, I propose a new method of physical biometric verification that uses dynamic data which are difficult to spoof. This basic concept can be applied to several types of biometric verification, such as those based on finger geometry, finger veins, irises, and the contour of the eyelid. I also propose an algorithm for this new verification method and provide experimental examples of its application.
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