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The development of contemporary navigation and positioning systems have significantly improved reliability and speeds in maritime navigation. At the same time, the vulnerabilities of these systems to cyber threats represent a remarkable issue to the safety of navigation. Therefore, the maritime community has raised the question of cybersecurity of navigation systems in recent years. This paper aims to analyse the vulnerabilities of the Global Navigation Satellite System (GNSS), Electronic Chart Display Information System (ECDIS) and Automatic Identification System (AIS). The concepts of these systems were developed at a time when cybersecurity issues have not been among the top priorities. Open broadcasts, the absence of or limited existence of data encryption and authentication can be considered as their primary security weaknesses. Therefore, these systems are vulnerable to cyber-attacks. The GPS as the data source of a ship’s position can relatively easily be jammed and/or spoofed, increasing the vulnerabilities of ECDIS and AIS. A systematic literature review was conducted for this article, supplemented by a SWOT analysis of the AIS service and particular case studies of recent cyber-attacks on these systems. The analysis of selected case studies confirmed that these systems could easily be spoofed and become a subject of data manipulation with significant consequences for the safety of navigation. The paper provides conclusions and recommendations highlighting the necessity for the users to be aware of the vulnerabilities of modern navigation systems.

The civil Global Positioning System (GPS) is vulnerable to spoofing because of its open signal structure. The performance of previous spoofing detection methods is often limited due to spoofing's strong concealment. In this study, a method is proposed to detect spoofing by analysing the features of improved signal quality monitoring (SQM) moving variance (MV), improved SQM moving average (MA), early‐late phase, carrier‐to‐noise ratio–MV and clock offset rate of receiver using Support Vector Machines. Then, the effectiveness of different kernel functions is compared along with other previous methods, revealing that our method outperforms previous methods when coarse Gaussian is used as kernel function. Specifically, the f1 score of the proposed method is improved by 3.22%, 12.85% and 35.72% in comparison with Back Propagation network, Ratio and Delta. The authors hope this work is beneficial for future research and for the implementation of GPS spoofing detection technology and high‐performance receiver, which is of great significance to maintain the normal operation of GPS.

In some wireless networks Received Signal Strength Indicator (RSSI) based device profiling may be the only viable approach to combating MAC-layer spoofing attacks, while in others it can be used as a valuable complement to the existing defenses. Unfortunately, the previous research works on the use of RSSI-based profiling as a means of detecting MAC-layer spoofing attacks are largely theoretical and thus fall short of providing insights and result that could be applied in the real world. Our work aims to fill this gap and examine the use of RSSI-based device profiling in dynamic real-world environments/networks with moving objects. The main contributions of our work and this paper are two-fold. First, we demonstrate that in dynamic real-world networks with moving objects, RSSI readings corresponding to one fixed transmitting node are neither stationary nor i.i.d., as generally has been assumed in the previous literature. This implies that in such networks, building an RSSI-based profile of a wireless device using a single statistical/ML model is likely to yield inaccurate results and, consequently, suboptimal detection performance against adversaries. Second, we propose a novel approach to MAC-layer spoofing detection based on RSSI profiling using multi-model Long Short-Term Memory (LSTM) autoencoder—a form of deep recurrent neural network. Through real-world experimentation we prove the performance superiority of this approach over some other solutions previously proposed in the literature. Furthermore, we demonstrate that a real-world defense system using our approach has a built-in ability to self-adjust (i.e., to deal with unpredictable changes in the environment) in an automated and adaptive manner.

The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms (PAD) into such systems. Companies and institutions need to know whether such integration would make the system more "secure" and whether the technology available is ready, and, if so, at what operational working conditions. Despite significant improvements, especially by adopting deep learning approaches to fingerprint PAD, current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modeling the cause-effect relationships when two non-zero error-free systems work together. Accordingly, this paper explores the fusion of PAD into verification systems by proposing a novel investigation instrument: a performance simulator based on the probabilistic modeling of the relationships among the Receiver Operating Characteristics (ROC) of the two individual systems when PAD and verification stages are implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms' ROCs submitted to the most recent editions of LivDet (2017-2019), the state-of-the-art NIST Bozorth3, and the top-level Veryfinger 12 matchers. Reported experiments explore significant scenarios to get the conditions under which fingerprint matching with embedded PAD can improve, rather than degrade, the overall personal verification performance.

Introduction/purpose: The paper provides a review of recent research in the field of GPS and ADS-B spoofing. Systems that rely on satellite positioning technology can be targeted by spoofing in order to generate incorrect positioning/timing, which is accomplished by inserting false signals into the "victim's" receiver. Attackers try to insert false positioning information into systems that, for example, provide navigation of airplanes or drones for the purpose of hijacking or distracting security/safety in airspace surveillance. New concepts of navigation and ATC will thus be necessary. Methods: Using a scientific approach, the paper gives an evaluation of GPS and ADS-B spoofing/antispoofing and how spoofing affects the cyber security of aviation systems. Results: Based on the methodological analysis used, the importance of studying spoofing/anti-spoofing in aviation is shown. Conclusion: Although spoofing in aviation is only a potential threat, its technical feasibility is realistic and its potential is considerable; it becomes more flexible and cheaper due to very rapid advancement of SDR technologies. The real risk, in the time to come, are potential spoofing attacks that could occur from the air, using drones. However, aircraft systems are not exposed to spoofing without any defense; receivers can detect it by applying various anti-spufing techniques. Also, pilots are able to detect and solve problems at every stage of the flight. However, due to a possibility of more sophisticated spoofing attacks, international organizations such as ICAO are proactively working to increase GPS and ADS-B systems robustness on spoofing.

Deteksi wajah merupakan proses mendasar dan penting dalam bidang pengenalan wajah yang sudah diteliti secara luas. Tujuan deteksi wajah adalah menentukan keberadaan dan menandai posisi wajah, baik pada gambar maupun video, yang disebut dengan bounding box. Salah satu masalah penting dalam deteksi wajah adalah membedakan wajah spoof dan non-spoof yang disebut sebagai deteksi spoofing wajah. Deteksi spoofing wajah merupakan pekerjaan penting yang digunakan untuk menjamin keamanan otentikasi berbasis wajah dan sistem analisis wajah. Oleh karena itu, dibutuhkan sebuah model yang dapat mendeteksi spoofing wajah. Pada makalah ini dilakukan proses membangun model yang dapat digunakan untuk mendeteksi wajah spoof dan non-spoof pada video menggunakan algoritme Faster R-CNN dengan arsitektur Resnet50. Faster R-CNN merupakan salah satu algoritme yang unggul dalam menyelesaikan berbagai persoalan deteksi objek. Dataset yang digunakan adalah Replay-Attack Database yang disediakan oleh Idiap Dataset Distribution Portal. Pada tahap training digunakan 360 video spoof dan non-spoof. Rata-rata nilai akurasi yang dihasilkan pada tahap training adalah 97,07%, dengan jumlah epoch sebanyak 21. Hasil pengujian menunjukkan bahwa model yang dihasilkan berhasil menentukan bounding box dengan akurat dan mendeteksi spoof dan non-spoof wajah pada video dengan efektif.

Spoofing of global-navigation satellite system (GNSS) signals to induce a target position estimate is a relevant security threat to the navigation of drones. However, spoofing multiple drones simultaneously as they move in a swarm, without disrupting their formation, is a complex task. In this paper, we propose to transmit spoofing signals from the ground, such that the fake position can be estimated in any point of an area of the plane where the swarm is moving. To this end we filter the satellite-generated GNSS signals with a multidimensional linear filter, and transmit the filtered signal with multiple ground antennas. The multidimensional filter is designed according to a generalized Wiener-filter criterion, such that the fake signal is accurately reproduced (in terms of mean squared error (MSE)) in the whole spoofed area. We investigate the impact of various design parameters (among others, the size of the spoofed area, the number of ground antennas, and the number of spoofed satellites) on both the MSE, and the probability of acquisition of the spoofed signal by the drones.

A large number of facts prove that Global Navigation Satellite System (GNSS) has certain security risks and is threatened by attacks. In particular, GNSS civilian signal receivers have some vulnerability in responding to spoofing attack and jamming attack, because the format and modulation of GNSS civilian signals are public. Based on the concerns of existing GNSS spoofing scenarios, this article reviews the research on GNSS anti-spoofing technologies from two aspects. In this article, the strategies of spoofing attacks discovered at this stage are classified in detail, the protection effect and scheme implementation complexity of various anti-spoofing techniques against spoofing attacks are analyzed from the perspective of signal-level and data-level. More specifically, we comprehensively analyze the ability for the combined anti-spoofing methods at the signal-level and the data-level to protect from combinations of different spoofing attack. Finally, the future development trend and potential research direction of GNSS anti-spoofing technology are summarized and predicted.

This paper analyzes candidate schemes for PPP/PPP-RTK data authentication. Data authentication asymmetric schemes are proposed based on existing standards and compatible with GNSS messages. Postquantum cryptographic signatures are also reviewed and discussed. Two schemes are selected for analysis: DS (digital signature), based on ECDSA, and DD (delayed-disclosure), based on a hybrid scheme using the TESLA protocol. An implementation of each of them is described in detail for both Galileo HAS (High Accuracy Service), and QZSS (Quasi-Zenith Satellite System) CLAS (Centimeter-Level Accuracy Service). The performance of the schemes in terms of time to receive the corrections message, and increase in the age of data (AOD) due to authentication, is analyzed. The analysis is complemented by a review of the CPU consumption at receiver level.

Background: Biometric sensing is a security method for protecting information and property. State-of-the-art biometric traits are behavioral and physiological in nature. However, they are vulnerable to tampering and forgery. Methods: The proposed approach uses blood flow sounds in the carotid artery as a source of biometric information. A handheld sensing device and an associated desktop application were built. Between 80 and 160 carotid recordings of 11 s in length were acquired from seven individuals each. Wavelet-based signal analysis was performed to assess the potential for biometric applications. Results: The acquired signals per individual proved to be consistent within one carotid sound recording and between multiple recordings spaced by several weeks. The averaged continuous wavelet transform spectra for all cardiac cycles of one recording showed specific spectral characteristics in the time-frequency domain, allowing for the discrimination of individuals, which could potentially serve as an individual fingerprint of the carotid sound. This is also supported by the quantitative analysis consisting of a small convolutional neural network, which was able to differentiate between different users with over 95% accuracy. Conclusion: The proposed approach and processing pipeline appeared promising for the discrimination of individuals. The biometrical recognition could clinically be used to obtain and highlight differences from a previously established personalized audio profile and subsequently could provide information on the source of the deviation as well as on its effects on the individual’s health. The limited number of individuals and recordings require a study in a larger population along with an investigation of the long-term spectral stability of carotid sounds to assess its potential as a biometric marker. Nevertheless, the approach opens the perspective for automatic feature extraction and classification.

Most localization systems rely on measurements gathered from signals emitted by stations whose position is assumed known as ground truth, namely anchors. As demonstrated by a significant bulk of experimental research, location security is threatened when an attacker becomes able to tamper either the signals emitted by the stations, or convince the user that the anchor station is in a different position than the true one. With this paper, we first propose a for- mal threat model which captures the above-mentioned wide class of attacks, and permits to quantitatively evaluate how tampering of one or more anchor locations undermines the user’s localization accuracy. We specifically derive a Cramér Rao Bound for the local- ization error, and we assess a number of example scenarios. We believe that our study may provide a useful formal benchmark for the design and analysis of detection and mitigation solutions.

Automatic Dependent Surveillance-Broadcast is an Air Traffic Control system in which aircraft transmit their own information (identity, position, velocity, etc.) to ground sensors for surveillance purposes. This system has many advantages compared to the classical surveillance radars: easy and low-cost implementation, high accuracy of data, and low renewal time, but also limitations: dependency on the Global Navigation Satellite System, a simple unencrypted and unauthenticated protocol. For these reasons, the system is exposed to attacks like jamming/spoofing of the on-board GNSS receiver or false ADS-B messages’ injection. After a mathematical model derivation of different types of attacks, we propose the use of a crowd sensor network capable of estimating the Time Difference Of Arrival of the ADS-B messages together with a two-step Kalman filter to detect these attacks (on-board GNSS/ADS-B tampering, false ADS-B message injection, GNSS Spoofing/Jamming). Tests with real data and simulations showed that the algorithm can detect all these attacks with a very high probability of detection and low probability of false alarm.

This paper takes a close look at the landscape of the Automatic Identification System (AIS) as a major source of information for maritime situational awareness (MSA) and identifies its vulnerabilities and challenges for safe navigation and shipping. As an important subset of cyber threats affecting many maritime systems, the AIS is subject to problems of tampering and reliability, indeed, the messages received may be inadvertently false, jammed, or intentionally spoofed. A systematic literature review was conducted for this article, complemented by a case study of a specific spoofing event near Elba in December 2019, which confirmed that the typical maritime AIS could be easily spoofed and generate erroneous position information. This intentional spoofing has affected navigation in international waters and passage through territorial waters. The maritime industry is neither immune to cyberattacks nor fully prepared for the risks associated with the use of modern digital systems. Maintaining seaworthiness in the face of the impact of digital technologies requires a robust cybersecurity framework.

Many infrastructures that shape our lifestyle depend on the GPS. Even though not used for positioning, these infrastructures need GPS to derive a precise time synchronization necessary for their operations. Examples are power distribution grids, gateways for financial transactions, and telecommunication networks. However, GPS receivers, and more in general g lobal navigation satellite system (GNSS) receivers, are vulnerable to interference. This makes GNSS-dependent infrastructures prone to time synchronization errors. This paper addresses the vulnerability of GNSS receivers to the intentional injection of counterfeit signals, an operation known as “spoofing.” The analysis is backed by a campaign of tests conducted on three receivers suitable for applications that use GNSS-based synchronization. A battery of spoofing attacks, targeting the control of the output time reference of these receivers, is purposely simulated with lab instrumentation. The effects of such attacks on the receivers’ measurements are observed and quantified; they prove that the vulnerability of GNSS receivers to malevolent radio attacks is a quantifiable reality, but such attacks “leave traces” that could trigger their detection. The purpose of this paper is to increase awareness, though not alarm, about the risk represented by time spoofing on application domains used to look at GNSS as a commodity. This paper shows that reducing this risk is possible at both receiver and system level.

Deception of navigational subsystems of a client, ranging from a hunting GPS receiver to the military-class GNSS receiver onboard of a Cruise Missile, is one of the probable electronic attacks. One of the simplest and practical way too deceive these receivers is to re-broadcast the delayed navigational signals. The authors were studied the feasibility of this method in the “Best” attainable conditions while the generality of study retained. The results from introduced geometrical and mathematical models were used to define the minimum and maximum operational range of Repeater Deception System parametrically. Then the parameters were substituted by information of various GNSS constellations. Investigation of reliability of selected EW scenario concluded the article.

The general accessibility and high accuracy of GPS caused that for a dozen or so years it is applied commonly, not only in marine navigation. We can ascertain that in this regard there exists the monopoly. However, now it is apparently that this system can be easily disturbed, what testify numerous reports. The problem has been treated as troubles in land navigation, however nowadays became every-day reality on coastal waters as well, especially on the Mediterranean and Black Seas and Persian Gulf. Officers who survived this tell that the first impulse in such situation is to verify GPS receiver, regardless of the situation around the ship. The concentration of the officer’s attention on the GPS receiver, especially on coastal waters creates the threat for the ship, however in this situation appear other threats which many officers does not associate with GPS. Usually on the present ship GPS receiver is not only the source of positioning information. It is a source of information for many other devices, so inappropriate work of it generates problems with many other processes on the ship. Today question is who on the bridge can notice GPS problems and how? There are receivers which do not inform about the problem, or present not realistic data. Sometimes only ECDIS picture shows some abnormality, for example still the same position while ship is under the way. On the paper the analysis of possibly aspects of the problem is discussed. Presented analysis goes to the conclusion that should be prepared some procedure how to proceed in case of the lack of GPS signals, as well as the watch officer should be prepared to act in such situation. This is a task for marine academies.

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.

Satellite-derived timing information plays a determinant role in the provisioning of an absolute time reference to telecommunications networks, as well as in a growing set of other critical infrastructures. In light of the stringent requirements in terms of time, frequency, and phase synchronization foreseen in upcoming access network architectures (i.e., 5G), Global Navigation Satellite System (GNSS) receivers are expected to ensure enhanced accuracy and reliability not only in positioning but also in timing. High-end GNSS timing receivers combined with terrestrial cesium clocks and specific transport protocols can indeed satisfy such synchronization requirements by granting sub-nanosecond accuracy. As a drawback, the network infrastructure can be exposed to accidental interferences and intentional cyber-attacks. Within this framework, the ROOT project investigates the effectiveness and robustness of innovative countermeasures to GNSS and cybersecurity threats within a reference network architecture.

Ultrasonic distance sensors use an ultrasonic pulse&rsquo, s time of flight to calculate the distance to the reflecting object. Widely used in industry, these sensors are an important component in autonomous vehicles, where they are used for such tasks as object avoidance and altitude measurement. The proper operation of such autonomous vehicles relies on sensor measurements, therefore, an adversary that has the ability to undermine the sensor&rsquo, s reliability can pose a major risk to the vehicle. Previous attempts to alter the measurements of this sensor using an external signal succeeded in performing a denial-of-service (DoS) attack, in which the sensor&rsquo, s reading showed a constant value, and a spoofing attack, in which the attacker could control the measurement to some extent. However, these attacks require precise knowledge of the sensor and its operation (e.g., timing of the ultrasonic pulse sent by the sensor). In this paper, we present an attack on ultrasonic distance sensors in which the measured distance can be altered (i.e., spoofing attack). The attack exploits a vulnerability discovered in the ultrasonic sensor&rsquo, s receiver that results in a fake pulse that is produced by a constant noise in the input. A major advantage of the proposed attack is that, unlike previous attacks, a constant signal is used, and therefore, no prior knowledge of the sensor&rsquo, s relative location or its timing behavior is required. We demonstrate the attack in both a lab setup (testbed) and a real setup involving a drone to demonstrate its feasibility. Our experiments show that the attack can interfere with the proper operation of the vehicle. In addition to the risk that the attack poses to autonomous vehicles, it can also be used as an effective defensive tool for restricting the movement of unauthorized autonomous vehicles within a protected area.

Nowadays, the Global Navigation Satellite Systems (GNSS) technology is not the primary means of navigation for civil aviation and Air Traffic Control, but its role is increasing. Consequently, the vulnerabilities of GNSSs to Radio Frequency Interference, including the dangerous intentional sources of interference (i.e., jamming and spoofing), raise concerns and special attention also in the aviation field. This panorama urges for figuring out effective solutions able to cope with GNSS interference and preserve safety of operations. In the frame of a Single European Sky Air traffic management Research (SESAR) Exploratory Research initiative, a novel, effective, and affordable concept of GNSS interference management for civil aviation has been developed. This new interference management concept is able to raise early warnings to the on-board navigation system about the detection of interfering signals and their classification, and then to estimate the Direction of Arrival (DoA) of the source of interference allowing the adoption of appropriate countermeasures against the individuated source. This paper describes the interference management concept and presents the on-field tests which allowed for assessing the reached level of performance and confirmed the applicability of this approach to the aviation applications. publishedVersion

Open service GNSS signals are vulnerable to a spoofing attack where counterfeit signals are transmitted to deliberately mislead a user receiver. When the spoofer tries to capture the receiver's tracking loops or pull them away from the genuine signals the correlation function becomes distorted or multiple correlation peaks are present. This effect is utilised by a special class of GNSS receiver which uses block processing with FFT correlators on each satellite tracking channel. The Airbus Multiple Tracking Locked Loop (MTLL) receiver is such a receiver and has the full 2D delay-Doppler correlation function available. To detect a spoofing attack, this paper proposes a method of decomposing the correlation function to estimate the parameters of the radio propagation channel experienced by a GNSS satellite signal. Once the channel parameters have been established, a maximum likelihood ratio test is performed to identify anomalous channel conditions and raise a spoofing alarm. The method has been tested against the simulations of representative scenarios. In addition the University of Texas Spoofing Battery data sets and data from a live signal testing have been included. From the test results, an evaluation is made of the false alarm and missed detection probabilities along with the time to alert. This allows conclusions to be drawn about the potential of the proposed method as an anti-spoofing countermeasure in safety-critical GNSS applications.

Global Navigation Satellite System (GNSS) meaconing and spoofing are being considered as the key threats to the Safety-of-Life (SoL) applications that mostly rely upon the use of open service (OS) signals without signal or data-level protection. While a number of pre and post correlation techniques have been proposed so far, possible utilization of the supervised machine learning algorithms to detect GNSS meaconing and spoofing is currently being examined. One of the supervised machine learning algorithms, the Support Vector Machine classification (C-SVM), is proposed for utilization at the GNSS receiver level due to fact that at that stage of signal processing, a number of measurements and observables exists. It is possible to establish the correlation pattern among those GNSS measurements and observables and monitor it with use of the C-SVM classification, the results of which we present in this paper. By adding the real-world spoofing and meaconing datasets to the laboratory-generated spoofing datasets at the training stage of the C-SVM, we complement the experiments and results obtained in Part I of this paper, where the training was conducted solely with the use of laboratory-generated spoofing datasets. In two experiments presented in this paper, the C-SVM algorithm was cross-fed with the real-world meaconing and spoofing datasets, such that the meaconing addition to the training was validated by the spoofing dataset, and vice versa. The comparative analysis of all four experiments presented in this paper shows promising results in two aspects: (i) the added value of the training dataset enrichment seems to be relevant for real-world GNSS signal manipulation attempt detection and (ii) the C-SVM-based approach seems to be promising for GNSS signal manipulation attempt detection, as well as in the context of potential federated learning applications.

The signals of navigation satellites are easily affected by spoofing interference, causing the wrong position, speed or Universal Time Coordinate of the receiver to be calculated. Traditional detection and suppression algorithms are used only to eliminate the spoofing signals, which may lead to an insufficient number of satellites for positioning. An adaptive spoofing suppression algorithm (ASSA) based on a multiple antenna array is proposed in this study. The ASSA can use the cross-correlation gain of multiple antenna array to adaptively generate nulling and realize the simultaneous suppression of multiple spoofing signals. Moreover, ASSA does not need to capture and track spoofing separately, thus reducing the complexity of implementation and calculation. Experiments were conducted to verify the proposed system under different conditions, and the results show that ASSA can suppress multiple spoofings with little impact on positioning performance. Under the condition of spoofing, ASSAs were (2.22 m, 2.41 m, 4.43 m) in the static test and (2.27 m, 2.43 m, 4.64 m) in the kinematic test, which are good positioning performances for both. In addition, the ASSA is applied before capturing signals, which is beneficial to identifying and eliminating spoofing earlier and faster.

Fingerprint recognition systems have proven significant performance in many services such as forensics, border control, and mobile applications. Even though fingerprint systems have shown high accuracy and user acceptance, concerns have raised questions about the possibility of having our fingerprint pattern stolen and presented to the system by an imposter. In this paper, we propose a dynamic presentation attack detection mechanism that seeks to mitigate presentation attacks. The adopted mechanism extracts the variation of global fingerprint features in video acquisition scenario and uses it to distinguish bona fide from attack presentations. For that purpose, a dynamic dataset has been collected from 11 independent subjects, 6 fingerprints per user, using thermal and optical sensors. A total of 792 bona fide presentations and 2772 attack presentations are collected. The final PAD subsystem is evaluated based on the standard ISO/. Considering SVM classification and 3 folds cross validation, the obtained error rates at 5% APCER are 18.1% BPCER for the thermal subset and 19.5% BPCER for the optical subset. This work was supported by the European Union's Horizon 2020 for Research and Innovation Program under Grant 675087 (AMBER).

Global navigation satellite systems (GNSS) are being the target of various jamming, spoofing, and meaconing attacks. This paper proposes a new statistical test for the presence of multiple spoofers based on range measurements observed by a plurality of receivers located on a rigid body platform. The relative positions of the receivers are known, but the location and orientation of the platform are unknown. The test is based on the generalized likelihood ratio test (GLRT) paradigm and essentially performs a consistency check between the set of observed range measurements and known information about the satellite topology and the geometry of the receiver constellation. Optimal spoofing locations and optimal artificial time delays (as induced by the spoofers) are also determined.Exact evaluation of the GLRT requires the maximum-likelihood estimates of all parameters, which proves difficult. Instead, approximations based on iterative algorithms and the squared-range least squares algorithm are derived. The accuracy of these approximations is benchmarked against Cramer-Rao lower bounds.Numerical examples demonstrate the effectiveness of the proposed algorithm and show that increasing the number of GNSS receivers makes the attack easier to detect. We also show that using multiple GNSS receivers limits the availability of optimal attack positions. Funding Agencies|Security-Link; SURPRISE project - Swedish Foundation for Strategic Research (SSF); Linkoping University

Поступила: 11.07.2020. Принята в печать: 25.07.2020. Received: 11.07.2020. Accepted: 25.07.2020. Positioning and navigation problems have proven to be well-developed in all areas of the Earth (on the surface of the lithosphere and hydrosphere, in the atmosphere, troposphere and stratosphere), except the hydrosphere. This article is devoted to the problem of a analysis of the state of the underwater positioning and navigation and practically the undeveloped problem of the underwater navigation safety. Here we restrict ourselves to analyzing the possibility of transferring the well-established GNSS technology to the underwater world. Unfortunately, GNSS signals attenuate very quickly in water, thus all GNSS-like technologies are of the local nature, based on the transformation of the electromagnetic wave of GNSS signals into an acoustic wave. Therefore, we use the term LNSS (Local Navigation Satellite Systems) to refer to this underwater technology. Проблемы позиционирования и навигации хорошо зарекомендовали себя во всех областях Земли (на поверхности литосферы и гидросферы, в атмосфере, тропосфере и стратосфере), за исключением гидросферы. Данная статья посвящена проблеме анализа состояния подводного позиционирования и навигации и практически неразвитой проблемы безопасности подводного плавания. Здесь мы ограничиваемся анализом возможности передачи устоявшейся технологии GNSS в подводный мир. К сожалению, сигналы GNSS очень быстро затухают в воде, поэтому все технологии, подобные GNSS, носят локальный характер и основаны на преобразовании электромагнитной волны сигналов GNSS в акустическую волну. Поэтому мы используем термин LNSS (Локальные навигационные спутниковые системы) для обозначения этой подводной технологии.

Global Positioning System (GPS) Spoofing attacks threaten technologies that our modern society depends on. To successfully develop defensive mechanisms against these attacks, methods to model the attacks and subsequently distinguish them from normal GPS operation must be developed. This paper primarily details the step-by-step implementation of a low-cost GPS spoofing and high-level spoofing data collection apparatus to model a simplistic spoofing attack that could be implemented with limited resources. The spoofing apparatus developed has been used to successfully attack a DJI Matrice 100 quadcopter and a portion of the collected spoofing data is presented.

The paper focuses on the use of GNSS constellation simulators for the performance evaluation of advanced anti-jamming and anti-spoofing techniques of GNSS receivers using multiple antennas in an antenna array. The use of antenna arrays and array signal processing enables a GNSS receiver to apply extremely efficient countermeasures to counteract radio frequency interference. This enhanced resilience to jamming and spoofing makes the multi-antenna GNSS receivers very attractive in the context of safety-critical applications. The paper highlights the advantages of testing such receivers in a controlled laboratory environment by utilizing the multi-antenna GNSS simulators. A fully scalable architecture of the multi-antenna simulator based on the use of multiple simulator units is presented. The simulator composed of 8 single RF output simulators is used together with a GNSS multi-antenna receiver prototype (GALANT) developed by DLR in order to obtain exemplary results for beamforming, direction of arrival estimation and spoofing detection in the corresponding signal scenarios. The obtained results are also used to highlight the merits of a GNSS array receiver as part of promising anti-jam and anti-spoof solutions.

The adoption of the global positioning system (GPS) within the marine industry has revolutionized the marine operations by condensing the navigation of a vessel into an integrated bridge system (IBS). An IBS acts as the main command and control of a vessel as it interconnects various digital devices used for navigation in open seas and is also connected to other on-board systems of a vessel e.g., navigation and control, propulsion and machinery management system, cargo management system and safety management system, core infra structure systems, administrative and crew welfare systems, etc. Additionally, it also provides a gateway to the Internet, thus, leaving not only an IBS vulnerable but also all the on-board systems vulnerable to cyber-attacks. We, in this study, have collected historical evidences about various vulnerable digital components in an IBS to better understand the security and privacy challenges associated with the vulnerable IBS components. Our study is the first of its kind that involves collection and review of 59 historical accidents reported in literature and has highlighted various vulnerability patterns, their causes and consequences, with geographical as well as temporal relationships for different vulnerable IBS components. The vulnerabilities of IBS components were reportedly exploited using various cyber-attack techniques e.g., jamming, spoofing, hijacking, etc. This review paper also forms a baseline for future work on vulnerabilities of IBS and maritime cyber security.

International audience; ASVspoof, now in its third edition, is a series of community-led challenges which promote the development of countermeasures to protect automatic speaker verification (ASV) from the threat of spoofing. Advances in the 2019 edition include: (i) a consideration of both logical access (LA) and physical access (PA) scenarios and the three major forms of spoofing attack, namely synthetic, converted and replayed speech; (ii) spoofing attacks generated with state-of-the-art neu-ral acoustic and waveform models; (iii) an improved, controlled simulation of replay attacks; (iv) use of the tandem detection cost function (t-DCF) that reflects the impact of both spoofing and countermeasures upon ASV reliability. Even if ASV remains the core focus, in retaining the equal error rate (EER) as a secondary metric, ASVspoof also embraces the growing importance of fake audio detection. ASVspoof 2019 attracted the participation of 63 research teams, with more than half of these reporting systems that improve upon the performance of two baseline spoofing countermeasures. This paper describes the 2019 database, protocols and challenge results. It also outlines major findings which demonstrate the real progress made in protecting against the threat of spoofing and fake audio.

This paper proposes low-complexity measures to be deployed on most aircraft to enable the management of Global Navigation Satellite Systems (GNSS) interference, and in particularly of jamming and spoofing threats, in order to reach the Flightpath2050 safety and security targets. It is known that, if there is a jamming interference and GNSS navigation is lost, a disruption will be caused, requiring the likely intervention of Air Traffic Control. Also, the presence of a spoofing signal is a serious security threat with a potentially catastrophic impact on the safety of the aircraft and on any other ground infrastructure. Through our jamming and spoofing detection and localization stages, based on minimal additional infrastructure, we will reduce the time required to detect and localize an interfering source and, therefore, the time required to mitigate it and restore the nominal traffic operations. Our solutions will also improve the safety of the Air Traffic Management system. Moreover, the deployment of our solution, with its capability of localizing an interfering device, could be a deterrent to any agent interested in intentionally generating a jamming or spoofing signal, and so will reduce the likelihood of this type of interference events.&nbsp

In this work, we simulate a scenario, where a publicly available ASV system is used to enhance mimicry attacks against another closed source ASV system. In specific, ASV technology is used to perform a similarity search between the voices of recruited attackers (6) and potential target speakers (7,365) from VoxCeleb corpora to find the closest targets for each of the attackers. In addition, we consider 'median', 'furthest', and 'common' targets to serve as a reference points. Our goal is to gain insights how well similarity rankings transfer from the attacker's ASV system to the attacked ASV system, whether the attackers are able to improve their attacks by mimicking, and how the properties of the voices of attackers change due to mimicking. We address these questions through ASV experiments, listening tests, and prosodic and formant analyses. For the ASV experiments, we use i-vector technology in the attacker side, and x-vectors in the attacked side. For the listening tests, we recruit listeners through crowdsourcing. The results of the ASV experiments indicate that the speaker similarity scores transfer well from one ASV system to another. Both the ASV experiments and the listening tests reveal that the mimicry attempts do not, in general, help in bringing attacker's scores closer to the target's. A detailed analysis shows that mimicking does not improve attacks, when the natural voices of attackers and targets are similar to each other. The analysis of prosody and formants suggests that the attackers were able to considerably change their speaking rates when mimicking, but the changes in F0 and formants were modest. Overall, the results suggest that untrained impersonators do not pose a high threat towards ASV systems, but the use of ASV systems to attack other ASV systems is a potential threat., Published in Computer Speech and Language. arXiv admin note: text overlap with arXiv:1811.03790

Satellite navigation systems such as the Global Positioning System (GPS)makes it possible for users to find their relative or absolute position. Thanks to its mobility and reliability, the GPS is used in many civil and military applications. However, the GPS does not provide an advanced level of security. Therefore, it could be potentially a target of attacks. With the development of new GPS attacks, the security knowledge has to grow at the same rate, so existing attacks can be detected by updated versions of receiver software or hardware. In this paper, a comparative analysis of GPS receiver resilience to software attacks is performed with the help of GNSS simulator from Spirent. The main objective of this work is to perform a sensitivity analysis of variables involved in calculation of position of the GPS receivers from different price bands that might be targeted by existing or future GPS attack. Variables making the biggest impact on calculated position are determined using the model. Experimentation validation of their influence is performed using selected receivers and corrupted signals generated by GNSS simulator. The testing is based on tuning the selected variables in order to simulate the theoretical error obtained from the sensitivity analysis. The results obtained from testing are discussed in order to analyse the behaviour of the considered GNSS receivers (including the premium class ones)and establish whether they provide a protection from existing or potential GPS attacks.

We consider technology-assisted mimicry attacks in the context of automatic speaker verification (ASV). We use ASV itself to select targeted speakers to be attacked by human-based mimicry. We recorded 6 naive mimics for whom we select target celebrities from VoxCeleb1 and VoxCeleb2 corpora (7,365 potential targets) using an i-vector system. The attacker attempts to mimic the selected target, with the utterances subjected to ASV tests using an independently developed x-vector system. Our main finding is negative: even if some of the attacker scores against the target speakers were slightly increased, our mimics did not succeed in spoofing the x-vector system. Interestingly, however, the relative ordering of the selected targets (closest, furthest, median) are consistent between the systems, which suggests some level of transferability between the systems., (A slightly shorter version) has been submitted to IEEE ICASSP 2019

The results of spoofing detection systems proposed during ASVspoof Challenges 2015 and 2017 confirmed the perspective in detection of unforseen spoofing trials in microphone channel. However, telephone channel presents much more challenging conditions for spoofing detection, due to limited bandwidth, various coding standards and channel effects. Research on the topic has thus far only made use of program codecs and other telephone channel emulations. Such emulations does not quite match the real telephone spoofing attacks. In order to asses spoofing detection methods in real scenario we present the PHONESPOOF dataset - spoofing data collected through realistic telephone channels. The PHONE-SPOOF data collection represents most threatening types of spoofing attacks and is publicly available dataset1. This work2 aimed to investigate robustness of the state-of-the-art deep learning based antispoofing systems under telephone spoofing attacks conditions based on the PHONESPOOF data. Moreover newly collected dataset makes it possible to analize language dependency issue for the Anti-Spoofing methods. In the work we also focused on the development of a unified LCNN-based approach for spoofing attack detection. The goal was to train a single system able to detect various types of spoofing attacks in telephone channel. The obtained results approve the effectiveness of such solution.

Electromagnetic interference can degrade civil GNSS signals and services, and in some cases result in integrity failures. The aviation community is well-aware of such threats due to the proliferation of interfering-capable equipment including personal electronic devices (PEDs), personal privacy devices (PPDs), GNSS repeaters, mis-operated test equipment, low-cost software-defined radio cards, and the foreseeable proliferation of more sophisticated spoofing devices in the future. Protection against such interference is under consideration for the next-generation of avionics standards. This paper is intended to support the definition of new avionics standards that address these new interference threats. First, it provides a current review of the technical literature with a focus on aviation. Then, the paper compiles the existing nomenclature, taking into account definitions from regulatory bodies. Next, the paper identifies categories of threat emissions. Later, the assets to protect and use case under analysis are presented, followed by a discussion of detection and mitigation responses. The paper continues with a list of detection and mitigation actions that can be implemented at different stages of the avionics receiver. Finally, the paper concludes with a proposal on whether to mandate or recommend treatment of specific threats in the next generation of standards. The framework presented in this paper is the result of several months of iterations including research institutes, GNSS providers, and air navigation service providers. This process was required to converge to a common nomenclature and threat categorization of new jamming and spoofing threats, which did not exist in the available literature and standards. Based on common framework, a quantitative risk analysis including the modelling of the threats is already being performed, enabling requirements and associated test procedures in the next generation of aviation standards.

As the demand for precision positioning grows around the world and spreads across various industries, initiatives are being taken to increasingly protect Global Navigation Satellite System (GNSS) receivers from intruders of all kinds, from unintentional industrial interference to advanced GNSS spoofing systems. The timing and cost of these forthcoming satellite navigation safety efforts are difficult to decipher due to the large number of new signals and constellations being deployed. However, it is safe to say that the newly designed anti-jamming and anti-spoofing GNSS systems open up new opportunities for innovative technologies. The false acoustic signal delay is equal to the sum of the spoofer receiving antenna delay, processing delay, and propagation delay from the spoofer to the victim. The victim finds the same location as the spoofer’s receiving antenna, and receivers located in different locations will have the same XYZ. The article presents classifications of terrorist attacks of this type.

We consider how to protect Unmanned Aerial Vehicles (UAVs) from Global Positioning System (GPS) spoofing attacks to provide safe navigation. The Global Navigation Satellite System (GNSS) is widely used for locating drones and is by far the most popular navigation solution. This is because of the simplicity and relatively low cost of this technology, as well as the accuracy of the transmitted coordinates. Nevertheless, there are many security threats to GPS navigation. These are primarily related to the nature of the GPS signal, as an intruder can jam and spoof the GPS signal. We discuss methods of protection against this type of attack and have developed an experimental stand and conducted scenarios of attacks on a drone&rsquo, s GPS system. Data from the UAV&rsquo, s flight log were collected and analyzed in order to see the attack&rsquo, s impact on sensor readings. From this we identify a new method for detecting UAV anomalies by analyzing changes in internal parameters of the UAV. This self-diagnosis method allows a UAV to independently assess the presence of changes in its own subsystems indicative of cyber attacks.

Many GNSS applications have been experiencing some constantly growing needs in terms of security and reliability. To address some of them, both GPS and Galileo are proposing evolutions of their legacy civil signals, embedding features of authentication. This paper focuses on the Galileo Open Signal Navigation Message Authentication (OSNMA) and describes its implementation within a real-time software receiver for ARM-based embedded platforms. The innovative contributions of the paper include the software profiling analysis for the OSNMA add on, along with the comparison among performances obtained with different platforms. In addition, specific evaluations on the computational load of the whole receiver complete the analysis. The receiver used for the implementation belongs to the NGene receivers family&mdash, real-time fully-software GPS and Galileo receivers, tailored for different platforms and sharing the same core processing. In detail, the paper deals with the introduction of the OSNMA support inside the eNGene, the version of the receiver executable by ARM-based embedded platforms.

Advancement in technology has increased the need to provide adequate security. Hence came the first security system using passwords, these systems have evolved and started using biometrics traits instead of passwords. But people have found ways to cheat such systems through different means. There have been many methods to prevent this security vulnerability, but these methods are very complex or not easy to implement. The most commonly used biometric trait for verification and also the most spoofed biometric trait is fingerprint. The need is to find an easy way to detect a fake fingerprint. The proposed system uses fingerprint and the photo of the fingernail when light is passed through is taken. The image of the fingernail is processed and features are extracted using SIFT and compared with feature vector stored in the database. The correlation is then calculated. Based on the correlation the system decides whether the fingerprint is fake or not. The system was found to react positively to intensity changes in the fingernail image and difference in the pattern of the fingernail. This system provides a simple and robust method for detection of fingerprint spoofing.

Mobile devices, such as smartphones, can offload applications and data to the cloud via access points or base stations to reduce energy consumption and improve user experience. However, mobile offloading is vulnerable to smart attackers that use smart and programmable radio devices, such as universal software radio peripherals, to perform multiple types of attacks, such as spoofing and jamming, based on the radio environment and offloading transmissions. In this paper, a mobile offloading game is investigated that consists of three players: a mobile device that chooses its offloading rate, a smart attacker that determines its attack mode, and a security agent that decides whether or not to initiate full protection for the serving access point during the offloading. Nash and Stackelberg equilibria of the offloading game are derived and their existence conditions are discussed. A Q-learning-based mobile offloading strategy is proposed for mobile devices that are unaware of system parameters, such as the channel conditions, in dynamic radio environments. Simulation results show that the proposed offloading strategy can improve the utility of the mobile device and reduce the attack rate of smart attackers.

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&rsquo, 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.

Power grids depend more and more on satellite-derived time and use signals from Global Navigation Satellite Systems (GNSSs) to be more efficient. GNSS signals serve to reference measurements to an absolute time scale, allowing for a better accuracy of the power grid state estimate. Phase Measurement Units (PMUs) take synchronous snapshots of current and voltage amplitudes/phases across the grid, returning a complete picture of the state of the power system at any time instant. Unfortunately, using conventional receiving hardware, GNSS signals transmitted by the satellites are well below the electromagnetic noise floor and are susceptible to interfering signals. Among all types of interference, intentional radio attacks (spoofing attacks) with the intent of disrupting GNSS services are becoming a significant concern, although no real incidents of this type have been publicly reported yet for power grids. This paper focuses on the threat posed by GNSS spoofing to PMUs. First, the paper critically discusses the vulnerability of some GNSS receivers, representative of those currently embedded in commercial PMUs. The relevance of the work consists in the methodology used to conduct the investigation. In fact, whereas spoofing effects on the computed positions have been reported in many papers, far less attention has been devoted to the computed time information and related applications. The paper intends to increase awareness, proving another fundamental aspect: the counterfeit signals leave clues in the receivers’ observables, thus giving the chance of transforming a feared vulnerability into a potential resilience, if these clues are detected and smartly exploited. Finally, the paper introduces the concept of authenticated civilian GNSS signals, namely the Galileo Open Service Navigation Message Authentication (OS NMA), which will be broadcast for signal experimentation starting from 2019.

The vulnerability of automatic speaker verification (ASV) systems to spoofing is widely acknowledged. Recent years have seen an intensification in research efforts to develop spoofing countermeasures, also known as presentation attack detection (PAD) systems. Much of this work has involved the exploration of features that discriminate reliably between bona fide and spoofed speech. While there are grounds to use different frontends for ASV and PAD systems (they are different tasks) the use of a single front-end has obvious benefits, not least convenience and computational efficiency, especially when ASV and PAD are combined. This paper investigates the performance of a variety of different features used previously for both ASV and PAD and assesses their performance when combined for both tasks. The paper also presents a Gaussian back-end fusion approach to system combination. In contrast to cascaded architectures, it relies upon the modelling of the two-dimensional score distribution stemming from the combination of ASV and PAD in parallel. This approach to combination is shown to generalise particularly well across independent ASVspoof 2017 v2.0 development and evaluation datasets.Index Terms: automatic speaker verification, spoofing, countermeasures, presentation attack detection

The use of GNSS positioning in highly automated systems, like packet delivering using drones or self-driving cars, demands a reliable position estimation. One major risk to the position estimation is the so called spoofing threat. A spoofer fakes satellite signals in order to mislead a GNSS receiver into estimating a wrong user position. A lot of research focuses on the detection and mitigation of spoofing by evaluating several metrics implemented in the receiver. However, beforehand it should be investigated under which conditions a receiver is affected by spoofing. In this work a simulation tool is described to simulate a scenario, where a receiver structure processes a signal containing an authentic and a spoofing signal. The parameters of the spoofing signal are variated. These simulations are repeated with different receiver settings in order to evaluate robust configurations for GNSS receivers. The results show that especially the coherent integration time in the receiver’s tracking loops has a large impact on the vulnerability of the receiver.

Nowadays, cyber attacks are becoming an extremely serious issue, which is particularly important to prevent in a smart city context. Among cyber attacks, spoofing is an action that is increasingly common in many areas, such as emails, geolocation services or social networks. Identity spoofing is defined as the action by which a person impersonates a third party to carry out a series of illegal activities such as committing fraud, cyberbullying, sextorsion, etc. In this work, a face recognition system is proposed, with an application to the spoofing prevention. The method is based on the Histogram of Oriented Gradients (HOG) descriptor. Since different face regions do not have the same information for the recognition process, introducing entropy would quantify the importance of each face region in the descriptor. Therefore, entropy is added to increase the robustness of the algorithm. Regarding face recognition, our approach has been tested on three well-known databases (ORL, FERET and LFW) and the experiments show that adding entropy information improves the recognition rate significantly, with an increase over 40% in some of the considered databases. Spoofing tests has been implemented on CASIA FASD and MIFS databases, having obtained again better results than similar texture descriptors approaches. This work was partially supported by the Ministerio de Economía y Competitividad (Spain), project TIN2013-40982-R, the FEDER funds, and the “Red de Investigación en el uso del aprendizaje colaborativo para la adquisición de competencias básicas. El caso Erasmus+ EUROBOTIQUE”, Red ICE 3701 curso 2016–2017.

Location information is one of the most vital information required to achieve intelligent and context-aware capability for various applications such as driverless cars. However, related security and privacy threats are a major holdback. With increasing focus on using Global Navigation Satellite Systems (GNSS) for autonomous navigation and related applications, it is important to provide robust navigation solutions, yet signal spoofing for illegal or covert transportation and misleading receiver timing is increasing and now frequent. Hence, detection and mitigation of spoofing attacks has become an important topic. Several contributions on spoofing detection have been made, focusing on different layers of a GNSS receiver. This paper focuses on spoofing detection utilizing self-contained sensors, namely inertial measurement units (IMUs) and vehicle odometer outputs. A spoofing detection approach based on a consistency check between GNSS and IMU/odometer mechanization is proposed. To detect a spoofing attack, the method analyses GNSS and IMU/odometer measurements independently during a pre-selected observation window and cross checks the solutions provided by GNSS and inertial navigation solution (INS)/odometer mechanization. The performance of the proposed method is verified in real vehicular environments. Mean spoofing detection time and detection performance in terms of receiver operation characteristics (ROC) in sub-urban and dense urban environments are evaluated.

Iris recognition is considered as one of the most promising noninvasive biometric systems providing automated human identification. Numerous programs, like unique ID program in India - Aadhar, include iris biometric to provide distinctive identity identification to citizens. The active area is usually captured under non ideal imaging conditions. It usually suffers from poor brightness, low contrast, blur due to camera or subject's relative movement and eyelid eyelash occlusions. Besides the technical challenges, iris recognition started facing sophisticated threats like spoof attacks. Therefore it is vital that the integrity of such large scale iris deployments must be preserved. This paper presents the development of a new spoof resistant approach which exploits the statistical dependencies of both general eye and localized iris regions in textural domain using spatial gray level dependence matrix (SGLDM), gray level run length matrix (GLRLM) and contourlets in transform domain. We did experiments on publicly available fake and lens iris image databases. Correct classification rate obtained with ATVS-FIr iris database is 100% while it is 95.63% and 88.83% with IITD spoof iris databases respectively.

GPS-based systems have been widely used in different critical sectors, including civilian and military applications. Despite of being able to provide great benefits, under certain circumstances they show to be highly vulnerable to intentional interferences. In this context, this article aimed to evaluate the susceptibility of different complex GPS-dependent systems to intentional interferences, focusing on the technique known as spoofing. This technique presents a high complexity and a great potential for damaging/deceiving complex systems, besides being difficult to identify and to implement countermeasures. Complex systems, like mobile phones, automobile receivers and aircraft receivers were submitted to different levels of spoofing, in free space and in a semi-anechoic chamber, being corrupted with low power levels of interference.