141 research outputs found

    Use of supervised machine learning for GNSS signal spoofing detection with validation on real-world meaconing and spoofing data : part I

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    The vulnerability of the Global Navigation Satellite System (GNSS) open service signals to spoofing and meaconing poses a risk to the users of safety-of-life applications. This risk consists of using manipulated GNSS data for generating a position-velocity-timing solution without the user's system being aware, resulting in presented hazardous misleading information and signal integrity deterioration without an alarm being triggered. Among the number of proposed spoofing detection and mitigation techniques applied at different stages of the signal processing, we present a method for the cross-correlation monitoring of multiple and statistically significant GNSS observables and measurements that serve as an input for the supervised machine learning detection of potentially spoofed or meaconed GNSS signals. The results of two experiments are presented, in which laboratory-generated spoofing signals are used for training and verification within itself, while two different real-world spoofing and meaconing datasets were used for the validation of the supervised machine learning algorithms for the detection of the GNSS spoofing and meaconing

    Detection solution analysis for simplistic spoofing attacks in commercial mini and micro UAVs

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    Enamus droone kasutab lennundusest pärit GPS navigatsiooniseadmeid, millel puuduvad turvaprotokollid ning nende riskioht pahatahtlike rünnakute sihtmärgina on kasvanud hüppeliselt lähimineviku arengute ja progressi tõttu SDR ja GNSS simulatsioonitarkvara valdkonnas. See on loonud ligipääsu tehnikale amatöörkasutajatele, millel on saatja aadressi võltsimise jõudlus. Need potensiaalsed rünnakud kuuluvad lihtsakoeliste kategooriasse, kuid selle uurimustöö tulemusena selgus, et nendes rünnakute edukuses on olulised erinevused teatud GPS vastuvõtjate ja konfiguratsioonide vahel. \n\rSee uurimustöö analüüsis erinevaid saatja aadressi võltsimise avastamise meetodeid, mis olid avatud kasutajatele ning valis välja need, mis on sobilikud mini- ja mikrodroonide tehnonõuetele ja operatsioonistsenaariumitele, eesmärgiga pakkuda välja GPS aadresside rünnakute avastamiseks rakenduste tasandil avatud allikakoodiga Ground Control Station tarkvara SDK. Avastuslahenduse eesmärk on jälgida ja kinnitada äkilisi, abnormaalseid või ebaloogilisi tulemväärtusi erinevates drooni sensiorites lisaallkatest pärit lisainfoga. \n\rLäbiviidud testid kinnitavad, et olenevalt olukorrast ja tingimustest saavad saatja aadressi võltsimise rünnakud õnnestuda. Rünnakud piiravad GPS mehanismide ligipääsu, mida saab kasutada rünnakute avastuseks. Neid rünnakuid puudutav info asetseb infovoos või GPSi signaalprotsessi tasandis, kuid seda infot ei saa haarata tasandile kus SDK tarkvara haldab kõigi teiste sensorite infot.Most of UAVs are GPS navigation based aircrafts that rely on a system with lack of security, their latent risk against malicious attacks has been raised with the recent progress and development in SDRs and GNSS simulation software, facilitating to amateurs the accessibility of equipment with spoofing capabilities. The attacks which can be done with this setup belong to the category simplistic, however, during this thesis work there are validated different cases of successful results under certain GPS receivers’ state or configuration.\n\rThis work analysis several spoofing detection methods found in the open literature, and selects the ones which can be suitable for mini and micro UAV technical specifications and operational scenario, for proposing a GPS spoofing detection solution developed in the application layer of an open source code Ground Control Station software SDK. The detection solution is intended to monitor and correlate abrupt, abnormal or unreasonable values of different sensors of the UAV with data obtained from available additional sources.\n\rThe conducted tests validate the cases and circumstances where the spoofing attacks were successful. Limitations include the lack of mechanisms to access GPS values which can be useful for detection spoofing attacks, but reside in the data bit or signal processing layer of the GPS and can not be retrieve to the layer where the SDK in computing all data of other sensors

    Signal processing techniques for GNSS anti-spoofing algorithms

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    The Global Navigation Satellite Systems (GNSS) usage is growing at a very high rate, and more applications are relying on GNSS for correct functioning. With the introduction of new GNSSs, like the European Galileo and the Chinese Beidou, in addition to the existing ones, the United States Global Positioning System (GPS) and the Russian GLONASS, the applications, accuracy of the position and usage of the signals are increasing by the day. Given that GNSS signals are received with very low power, they are prone to interference events that may reduce the usage or decrease the accuracy. From these interference, the spoofing attack is the one that has drawn major concerns in the GNSS community. A spoofing attack consist on the transmission of GNSS-like signals, with the goal of taking control of the receiver and make it compute an erroneous position and time solution. In the thesis, we focus on the design and validation of different signal processing techniques, that aim at detection and mitigation of the spoofing attack effects. These are standalone techniques, working at the receiver’s level and providing discrimination of spoofing events without the need of external hardware or communication links. Four different techniques are explored, each of them with its unique sets of advantages and disadvantages, and a unique approach to spoofing detection. For these techniques, a spoofing detection algorithm is designed and implemented, and its capabilities are validated by means of a set of datasets containing spoofing signals. The thesis focuses on two different aspects of the techniques, divided as per detection and mitigation capabilities. Both detection techniques are complementary, their joint use is explored and experimental results are shown that demonstrate the advantages. In addition, each mitigation technique is analyzed separately as they require specialized receiver architecture in order to achieve spoofing detection and mitigation. These techniques are able to decrease the effects of the spoofing attacks, to the point of removing the spoofing signal from the receiver and compute navigation solutions that are not controlled by the spoofer and lead in more accurate end results. The main contributions of this thesis are: the description of a multidimensional ratio metric test for distinction between spoofing and multipath effects; the introduction of a cross-check between automatic gain control measurements and the carrier to noise density ratio, for distinction between spoofing attacks and other interference events; the description of a novel signal processing method for detection and mitigation of spoofing effects, based on the use of linear regression algorithms; and the description of a spoofing detection algorithm based on a feedback tracking architecture
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