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The engagement of customers on websites should be at the heart of e-commerce because driving traffic to a website or platform enhances the opportunity for better branding and growth in revenues. Through a review of literature and practical observations of various websites, this paper explains engagement for e-commerce to increase traffic on websites and its benefits to companies. For this reason, we have summarized several important factors of success in e-commerce and the trends of consumer behavior in visiting websites. The results of this research are the accumulation of fundamental strategies and recommendations to enhance e-commerce business in the modern workplace. Electronic commerce (e-commerce) is a growing business that will most certainly continue to grow in the future. We are amid the continued growth of e-commerce, which indicates that the e-commerce business has become more competitive. It is mandatory to expand engagement for companies to attract customers, to retain them, and to increase sales as much as possible in an ethical and sustainable manner. A website or platform with more traffic means more opportunities to sell products or services accordingly. Due to the current economy being hurt by the Covid-19 coronavirus infection and quarantine restrictions, and the daily use of social media, consumers are changing their behavior to access more information in the online world, particularly through engaging and attractive websites. As such, relevant suggestions and recommendations are offered throughout this paper to create awareness regarding the importance of customer engagement in c-commerce.

GNSS is an indispensable source of positioning, navigation and timing for many sectors, including inland waterway transport. Unfortunately, GNSS is also vulnerable to interference, including intentional jamming and spoofing. This paper evaluates the vulnerability of one of the key inland waterway systems—the automatic identification system (AIS)—to GNSS jamming and spoofing. The vulnerability is explored via a series of tests conducted in both laboratory and live-sky environments. The results clearly show the negative impact of both types of interference on AIS. The impact included denial of service and reporting of false position. Additionally, the effects on subsequent systems like river information services or nearby vessels are also showcased. The results presented provide valuable insight into the vulnerability of inland waterway transport. The need for understanding the system limitations and vulnerability rises with the increase in the implementation of autonomous systems into the inland waterway sector, as well as other critical infrastructure sectors.

With the large-scale usage of satellite navigation, spoofing and jamming are considerable threats to civilian society. Recent developments, such as Galileo’s Open Service Navigation Message Authentication and GPS’s Chimera, mitigate these risks. However, they provide authentication of the navigation message or ranging code, but not a true position in the case of interference. In critical applications, a protected navigation service is desired, such as Galileo’s Public Regulated Service (PRS). PRS provides an access-controlled navigation service for authorized governmental users, with fully encrypted ranging codes and data channels, providing users with higher robustness against interference. The main challenge of implementing PRS on a large scale is the need to protect the cryptographic material that is required to access the PRS signals inside the receiver. For many applications, a stand-alone receiver solution is unnecessary. These applications could use a remote server for PRS. In this methodology, the end-user device has only a radio frequency front-end which sends short samples to a secure server. The (classified) signal processing is then carried out on this secure server, removing the need for the user device to protect cryptographic material. Besides decreasing the device’s security requirements and power consumption, it also allows to utilize the advantages of PRS in applications that would otherwise not be able to use PRS. In this approach the PRS usage authorization would only be required for the server operations, and not for the end-user devices. It furthermore allows for using additional processing power for unaided PRS acquisition in case of interference. Within the Netherlands, a remote server solution is developed by CGI: S-TrackS, making PRS accessible. In this paper, the application of PRS and architecture for various use cases is presented. It is shown that PRS usage based on a remote server is feasible and can increase the robustness for governmental applications.

Jammertest 2022 was a week-long series of satellite navigation and timing signal jamming and spoofing exercises carried out on the Norwegian island of Andøya in September of 2022. Organized via a collaboration between the Norwegian spectrum management authority, defense research establishment, public roads administration, metrology service, and others, the result was the largest known GNSS jamming and spoofing event open to international collaboration and provided an open-access data and publication policy for participants. This paper reviews the event’s organization, scheduled tests, noteworthy jamming observations, noteworthy spoofing observations, and the unexpected observations found during the event and also presents information on what data are publicly available to interested parties, along with the contact information needed to obtain these data.

Global Navigation Satellite System (GNSS) interference poses an increasing threat for civil aviation, and the detection and mitigation of interferences can help to make the sector more robust. This paper focuses on the detection and mitigation capabilities of a software-based Controlled Reception Pattern Antenna (CRPA) as part of a wider study in which different detection and mitigation methods are tested and compared. The proposed CRPA uses eigenvalue decomposition to determine the weight vector and is combined with MUltiple SIgnal Classification (MUSIC) for detection purposes. Simulations are used to test the software CRPA for its robustness against different types of interference in static and dynamic scenarios. The test method and processing pipeline are described. Initial results show the CRPA algorithm under test is capable of detecting and mitigating different types of interferences, and mitigation can help a receiver to maintain a position velocity time (PVT) solution for higher levels of interference power.

Resilience against signal disturbances is an important characteristic of GNSS-based PNT solutions. In particular, for critical infrastructures, failure to provide correct PNT information in these domains may have a major societal impact. The Resilience Framework by the Department of Homeland Security (DHS) provides a set of requirements and guidelines to design a PNT solution of a certain level of resilience. Over the lifetime of the applications, it will be of prime importance to assess the resilience of the PNT solutions on a regular basis. Given how often GNSS-based solutions are being applied, partly automating the assessment process will be needed to make this task feasible. To automate the generative process, a machine-readable structure with well-established meaning is required. In this work, the use of fault trees as a formal system to encode the resilience framework is investigated.

The past decade has shown a growing awareness of the dangers of intentional interference (especially jamming and spoofing) with GNSS signals. The Antiference project uses reconfigurable digital signal processing methods in the detection, classification, and mitigation of interference by employing machine learning techniques. The ML-based jamming classifier uses distinctive features of spectrograms for the differentiation of various jamming attacks. A residual neural net is used to map the spectrograms to the different jamming types. It relies on a fingerprinting architecture. Fingerprints summarize the characteristics of all the incoming signals, which are stored in and matched to a database of previously encountered interference types. To validate the implemented functionalities, a developed test-bed runs test scenarios and benchmarks the results against two state-of-the-art COTS receivers with interference mitigation capabilities.

With the development of satellite navigation technology, the research focus of GNSS has shifted from improving positioning accuracy to expanding system application and improving system performance. At the same time, improving the survivability of satellite navigation systems has become a research hotspot in the field of navigation, especially with regard to anti-spoofing. This paper first briefly analyzes the common interference types of satellite navigation and then focuses on spoofing. We analyze the characteristics and technical mechanism of satellite navigation and the positioning signal. Spoofing modes are classified and introduced separately according to signal generation, implementation stage and deployment strategy. After an introduction of GNSS spoofing technology, we summarize the research progress of GNSS anti-spoofing technology over the last decade. For anti-spoofing technology, we propose a new classification standard and analyze and compare the implementation difficulty, effect and adaptability of the current main spoofing detection technologies. Finally, we summarize with considerations, prospective challenges and development trends of GNSS spoofing and anti-spoofing technology in order to provide a reference for future research. [ABSTRACT FROM AUTHOR]

Global navigation satellite systems (GNSSs) are the most significant service for global positioning and timing. The high relevance and wide spread of these systems contrast with the risk for interference or even manipulations of GNSS signals. One specific threat is GNSS spoofing. A spoofer counterfeits satellite signals to mislead the receiver to an erring position/time estimation. The technological progress enabling affordable and easy-to-use spoofer hardware further increases the relevance of this threat. To maintain the integrity of the position/ time information, it is mandatory to be able to assess the errors induced by spoofing. The paper at hand derives a bound of the code tracking bias in relevant spoofing scenarios extending the well-known Multipath Error Envelope. These new bounds can be used as a tool to estimate the position/time error, especially but not exclusively for receivers that are collateral damage of a spoofing attack. [ABSTRACT FROM AUTHOR]

In modern GNSS receivers, the Automatic Gain Control (AGC) monitors the received signal level to optimize quantization and mitigate interference. This paper characterizes the jamming and spoofing impact on AGC and received signal. It first expresses the AGC gain as a function of the received signal level. Under nominal conditions, the AGC leverages the ergodic properties of the received signal to estimate its level over time. Two physical quantities, namely time-based power and signal distribution, are typically considered. However, in the presence of interference, these ergodic properties are no longer guaranteed, posing challenges in modeling the behavior of these quantities. This paper proposes a probabilistic framework for interpreting temporal estimation and computing time-based power and distribution in order to characterize AGC gain under jamming and spoofing. First, this study models the spoofing impact for both unique and multiple emitted spoofing signals as a function of the re-radiated noise power and the spoofing signals' characteristics (e.g., number of emitted signals, amplitudes, modulation). Furthermore, it reveals the non-uniformity of the jamming chirp phase, which introduces distortions in power and signal distribution, consequently affecting AGC gain and demonstrates the convergence of the jamming signal toward a continuous wave signal at high frequencies. • Expression of the AGC dynamic behavior as a function of the received signal level. • Definition of a new probabilistic framework to interpret time-based estimation. • Approximation of multiple spoofing signals into a Gaussian model. • Highlighting the non-uniformity and non-ergodicity of chirp phase. • Approximation of the chirp signal as a continuous wave at intermediate frequency. [ABSTRACT FROM AUTHOR]

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 navigation of small unmanned aerial vehicles (UAVs) mainly depends on global positioning systems (GPSs). However, GPSs are vulnerable to attack by spoofing, which causes the UAVs to lose their positioning ability. To address this issue, we propose a deep learning method to detect the spoofing of GPS signals received by small UAVs. Firstly, we describe the GPS signal dataset acquisition and preprocessing methods; these include the hardware system of the UAV and the jammer used in the experiment, the time and weather conditions of the data collection, the use of Spearman correlation coefficients for preprocessing, and the use of SVM-SMOTE to solve the spoofing data imbalance. Next, we introduce a PCA-CNN-LSTM model. We used principal component analysis (PCA) of the model to extract feature information related to spoofing from the GPS signal dataset. The convolutional neural network (CNN) in the model was used to extract local features in the GPS signal dataset, and long short-term memory (LSTM) was used as a posterior module of the CNN for further processing and modeling. To minimize randomness and chance in the simulation experiments, we used the 10-fold cross-validation method to train and evaluate the computational performance of our spoofing machine learning model. We conducted a series of experiments in a numerical simulation environment and evaluated the proposed model against the most advanced traditional machine learning and deep learning models. The results and analysis show that the PCA-CNN-LSTM neural network model achieved the highest accuracy (0.9949). This paper provides a theoretical basis and technical support for spoofing detection for small-UAV GPS signals.

In this paper, we present an innovative physical layer authentication approach for underwater acoustic communication networks. Our method leverages the transmitter nodes' positions to establish a robust authentication mechanism. We consider two scenarios based on the mobility of the nodes. In the first scenario, underwater nodes (both legitimate and malicious) are static, while the second scenario assumes that the underwater nodes are mobile moving at a certain velocity. For both scenarios, we estimate position by analyzing the signals received at reference nodes that are strategically placed within a predetermined underwater area. Once the estimates are available, we propose binary hypothesis testing based on the estimated position to determine the legitimacy of the transmitter node. Furthermore, when the nodes are mobile, we perform velocity estimation at a certain time by taking the difference of the estimated coordinates, for which we also find the uncertainty in the estimation. We use a linear Kalman filter operation to track the legitimate node's mobility. We provide closed-form expressions of the false alarm rate and missed detection rate resulting from binary hypothesis testing. We validate our proposed mechanism through simulation, demonstrating error behavior against link quality, malicious node location, and receiver operating characteristic (ROC) curves. [ABSTRACT FROM AUTHOR]

Face recognition is an important application of pattern recognition and image analysis in biometric security systems. The COVID-19 outbreak has introduced several issues that can negatively affect the reliability of the facial recognition systems currently available: on the one hand, wearing a face mask/covering has led to growth in failure cases, while on the other, the restrictions on direct contact between people can prevent any biometric data being acquired in controlled environments. To effectively address these issues, we designed a hybrid methodology that improves the reliability of facial recognition systems. A well-known Source Camera Identification (SCI) technique, based on Pixel Non-Uniformity (PNU), was applied to analyze the integrity of the input video stream as well as to detect any tampered/fake frames. To examine the behavior of this methodology in real-life use cases, we implemented a prototype that showed two novel properties compared to the current state-of-the-art of biometric systems: (a) high accuracy even when subjects are wearing a face mask; (b) whenever the input video is produced by deep fake techniques (replacing the face of the main subject) the system can recognize that it has been altered providing more than one alert message. This methodology proved not only to be simultaneously more robust to mask induced occlusions but also even more reliable in preventing forgery attacks on the input video stream. [ABSTRACT FROM AUTHOR]

Digital Twin is the mirror image of any living or non-living objects. Digital Twin and Cyber-physical system (CPS) provides a new era for industries especially in the healthcare sector that keeps track of health data of individuals to provide on-demand, fast and efficient services to the users. In the suggested system, various health parameters of the patients are collected through different health instruments, wearable devices that communicate data to the primary database; used for analysis purposes for better diagnosis and training for automated systems. The primary database in a physical object and parallelly maintain virtual object/digital twin of the same in order of analyzing, summarize and mine data for diagnosis, monitoring the patient in real-time. The e-health cloud data need to be protected from unauthorized access by biometric authentication using iris biometric trait. The proposed paper suggested two phases EfficientNet Convolution Neural Network-based framework for identifying the real or spoofed user sample. The proposed system is trained using EfficientNet Convolution Neural Network on different datasets of spoofed and actual iris biometric samples to discriminate the original and spoofed one. [ABSTRACT FROM AUTHOR]

Face recognition is used in biometric systems to verify and authenticate an individual. However, most face authentication systems are prone to spoofing attacks such as replay attacks, attacks using 3D masks etc. Thus, the importance of face anti-spoofing algorithms is becoming essential in these systems. Recently, deep learning has emerged and achieved excellent results in challenging tasks related to computer vision. The proposed framework relies on the extraction of features from the faces of individuals. The approach relies on dimensionality reduction and feature extraction of input frames using pre-trained weights of convolutional autoencoders, followed by classification using softmax classifier. Experimental analysis on three benchmarks, Idiap Replay Attack, CASIA- FASD and 3DMAD, shows that the proposed framework can attain results comparable to state-of-the-art methods in both cross-database and intra-database testing. [ABSTRACT FROM AUTHOR]

Spoofing attacks on the fingerprint scanners become major and serious concern with the growing development and use of biometric technologies. Level 1 and Level 2 features; which are said to be unique and most commonly used features in fingerprint verification systems, are easily get spoofed. Moreover, single feature based specifically designed spoof detection methods are not performed well on different fingerprint scanners and spoofing materials. This paper proposed the fusion of pores perspiration and texture features in static software based approach to identify live and fake fingerprints. The pores perspiration activity is quantified by computing the ridge signal energy and gray level distributions around the detected pores. These pore characteristics are statically determined instead of dynamic measurement. Autoencoder neural network is used to reduce the high dimensional feature vector and learn its low dimensional hidden representation unsupervisedly. The binary classification in two classes: live and spoof is performed by the supervisedly trained softmax classifier. The performance of the classifier is evaluated in terms of Average Classification Error (ACE) and misclassification rates: FerrLive and FerrFake. The experimental results carried out on LivDet 2013 and LivDet 2015 databases show the improvement of the classifier performance in comparison to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Copyright of Gândirea Militară Românească is the property of Romanian Armed Forces Defence Staff and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the current geopolitical context, the Black Sea region has become the scene of the conflict in which a wide range of weapons and ammunition are used. It is their directing/guiding by using signals transmitted by satellite navigation networks – GNSS that categorically makes the difference. Their contribution can be instantly noticed, even though it is not a novelty. The weapons that benefited from the augmentation of the satellite signal proved the accuracy of their shots. This is the reason why the actions of jamming or falsification of the satellite signal appeared in the battlefield and even threats of GNSS attack were launched. In the present article, I intend a disambiguation of the subject, in an attempt to delimit military declarations from political ones, in the space environment, which has become essential in the conduct of modern military actions. [ABSTRACT FROM AUTHOR]

The ARP protocol is used to determine the MAC Address of a device whose IP address is known. When a device wants to interact with another device on the network, it uses ARP to determine the MAC Address of the device with which it wishes to communicate. The ARP poisoning or ARP spoofing technique is used in the MITM attack. This is accomplished by taking advantage of two security flaws. The first is that each ARP request or response is regarded as legitimate. Simply inform any device on your network that you are the router, and the device will trust you. The simulated data is displayed as a trace graph, which contains the communication records. The trace graph's standard trace format contains 54 features that display all of the packet communication's details. The ConvLSTM model can utilize the data once it has been pre-processed since it removes the unneeded data. The Convolutional LSTM (ConvLSTM) model is an extended form of the LSTM (Long Short-Term Memory) model, which is itself an enhanced version of RNN (Recurrent Neural Network). The proposed the Hybrid ConvLSTM-ECC method, which uses convolutional layers for feature extraction from raw data to detect the Data Link layer's ARP Spoofing-based MITM attack nodes in a wired and wireless context. The output is given into the LSTM model, which predicts detection accuracy and mitigates ARP Spoofing-based MITM attacks by producing signatures for node authentication using the data. [ABSTRACT FROM AUTHOR]

The broadcast nature of wireless communication makes it vulnerable to various security threats such as spoofing attacks. Physical layer (PL) authentication has emerged as a promising and powerful approach to secure future wireless technologies for next-generation communication networks. In this work, we propose a PL authentication scheme against spoofing attacks based on a novel distance signature that exploits the properties of beamspace multiple-input multiple-output (MIMO) channels in millimeter-wave (mmWave) networks. The proposed signature is derived from the positions of the principal components in beamspace channel domain by measuring their displacement from the original point and sorting the distance values in descending order based on the phases of the principal components. In addition, a mutual coupling effect is introduced into the system, which is a hardware property of multiple antenna design. This is then combined with the proposed distance signature to form a hybrid signature that further improves the authentication performance. Simulation results have confirmed the validity and effectiveness of our proposed system in terms of detection rate and false alarm. [ABSTRACT FROM AUTHOR]

Speaker recognition aims to recognize the identity of the speaking person. After decades of research, current speaker recognition systems have achieved rather satisfactory performance, and have been deployed in a wide range of practical applications. However, a massive amount of evidence shows that these systems are susceptible to malicious fake actions in real applications. To address this issue, the research community has been responding with dedicated countermeasures which aim to defend against fake actions. Recently, there are several reviews and surveys reported in the literature that describe the current state-of-the-art research advancements. Even so, these reviews and surveys are generally based on a canonical taxonomy to categorize spoofing attacks and corresponding countermeasures from the technology-oriented perspective. This paper provides a new taxonomy from the application-oriented perspective and extends to two major fake forms: spoofing attack and disguise cheating. This taxonomy starts from the applications of speaker recognition technology, e.g., access control, surveillance and forensic, and then rezones two fake forms according to different application scenarios: one is spoofing attack that imitates the voice of an authorized speaker to get access to the target system; the other one is disguise cheating that makes someone unrecognizable by altering his/her voice. Furthermore, for each fake form, more delicate categories and related countermeasures are presented. Finally, this paper discusses future research directions in this area and suggests that the research community should not only focus on the technical view but also connect with application scenarios. [ABSTRACT FROM AUTHOR]

Data security and privacy play a significant role in human life over the past few years. In the present digital era, advanced technologies utilize wide reliance and ubiquity to assist the counter theft system. Due to the enhanced crime rate, determining the solution becomes a burdensome process to recognize the fingerprint. To overcome such shortcomings, this paper proposes a convolution neural network and dynamic differential annealing (CNN-DDA)-based spoofed fingerprint detection. Here a CNN-DDA approach is proposed to analyze and evaluate the false or forged fingerprint concerning spoof forgery authentication system. The main intention of CNN-DDA architecture employs in investigating a complicated and problematic relationship among various features thus enabling highly detailed features. The proposed CNN-DDA-based spoofed fingerprint detection uses various datasets namely LivDet 2015 and LivDet 2013 for evaluation. Also, the real image set is captured using various fingerprint scanners such as Gelatine, wood glue, ecoflex and modasil. The experimental analysis is conducted for various evaluation measures such as accuracy rate, classification error value rate and processing time. The results revealed that the proposed approach provides high spoofed fingerprint detection with a better accuracy rate, less processing time and classification error. [ABSTRACT FROM AUTHOR]

Dynamic navigation devices like Global Positioning Systems (GPSs) are deployed for various purposes in different areas and these devices are usually the central point of interest of various groups like hackers to exploit the data sent and received by GPS systems. The GPS data is usually manipulated using spoofing attacks. This paper proposes a robust solution to the spoofing attacks carried out to manipulate, control and modify the location sharing of smart containers. The primary focus of this paper is securing the GPS information shared by the smart containers. The location shared by the smart containers is secured by encrypting it with Pretty Good Privacy (PGP) algorithm to avoid spoofing attacks in particular. The encrypted GPS location is sent across any communication channel. The receiver side will decrypt the encrypted GPS location at the receiving end. Hence, using this method of PGP encryption will ensure the safe and secure sharing of GPS location by the smart containers. As a result, GPS security has been improved by 80%. [ABSTRACT FROM AUTHOR]

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 аnd ADS-B systems robustness on spoofing.

We consider Global Navigation Satellite Systems (GNSS) spoofing attacks and devise a countermeasure appropriate for mobile GNSS receivers. Our approach is to design detectors that, operating after the signal acquisition, enable the victim receiver to determine with high probability whether it is under a spoofing attack or not. Namely, the binary hypothesis is that either the GNSS receiver tracks legitimate satellite signals, $\mathcal {H}_{0}$ , or spoofed signals, $\mathcal {H}_{1}$ . We assume that there exists an unknown number of multiple spoofers in the environment and the attack strategy (which legitimate signals are spoofed by which spoofers) is not known to the receiver. Based on these assumptions, we propose an algorithm that identifies the number of spoofers and clusters the spoofing data by using Bayesian information criterion (BIC) rule. Depending on the estimated and clustered data we propose a detector, called as generalized likelihood ratio (GLRT)-like detector. We compare the performance of the GLRT-like detector with a genie-aided detector in which the attack strategy and the number of spoofers is known by the receiver. In addition to this, we extend the GLRT-like detector for the case where the noise variance is also unknown and present the performance results.

Aircraft systems that rely on satellite positioning technology, such as GNSS and ADS-B, can be the target of a spoofing attack - a sophisticated and very dangerous form of radio frequency interference in which false signals are inserted into the "victim's" receiver for incorrect positioning or timing. Although spoofing in civil aviation is a potential threat, its technical feasibility is realistic, and the application of spoofing is becoming more flexible due to the very rapid progress of cheap SDR platforms. In particular, the potential risk is posed by potential air strikes, using unmanned aerial vehicles/drones, for the purpose of hijacking or distracting security in airspace surveillance. However, aviation is not ruthlessly exposed to spoofing attacks without any defense; by applying certain methods/techniques, spoofing can be mitigated in the GNSS receiver. Also, pilots are trained to detect and solve problems at every stage of the flight. Due to more sophisticated forms of terrorist attacks are possible, international organizations, such as ICAO and EUROCA, are proactively working to increase the robustness of the GNSS and ADS-B systems to spoofing. Given the importance of the topic and the fact that spoofing/antispuffing testing has certain limitations, consideration of the specifics and different scenarios of these attacks are very important in the development of new methods for their mitigation and detection. This paper focuses on spoofing/antispuffing of GNSS and ABS-B systems in civil aviation and provides an overview of the latest research in these areas.

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• Inter-frame 2D affine motion compensation is exploited for detecting 2D face artefacts. • Temporal Sequence Sampling (TSS) is proposed to encode a video into a single image. • A self-supervised learning scheme is presented for face presentation attack detection. • Promising generalization is achieved in cross-database tests on public benchmarks. Conventional 2D face biometric systems are vulnerable to presentation attacks performed with different face artefacts, e.g., printouts, video-replays and wearable 3D masks. The research focus in face presentation attack detection (PAD) has been recently shifting towards end-to-end learning of deep representations directly from annotated data rather than designing hand-crafted (low-level) features. However, even the state-of-the-art deep learning based face PAD models have shown unsatisfying generalization performance when facing unknown attacks or acquisition conditions due to lack of representative training and tuning data available in the existing public benchmarks. To alleviate this issue, we propose a video pre-processing technique called Temporal Sequence Sampling (TSS) for 2D face PAD by removing the estimated inter-frame 2D affine motion in the view and encoding the appearance and dynamics of the resulting smoothed video sequence into a single RGB image. Furthermore, we leverage the features of a Convolutional Neural Network (CNN) by introducing a self-supervised representation learning scheme, where the labels are automatically generated by the TSS method as the stabilized frames accumulated over video clips of different temporal lengths provide the supervision. The learnt feature representations are then fine-tuned for the downstream task using labelled face PAD data. Our extensive experiments on four public benchmarks, namely Replay-Attack, MSU-MFSD, CASIA-FASD and OULU-NPU, demonstrate that the proposed framework provides promising generalization capability and encourage further study in this domain. [ABSTRACT FROM AUTHOR]

Spoofing denotes attempts to cheat biometric technologies with artefacts (e.g. fake fingers, masks). This way of circumventing biometric systems has recently generated great interest in the line of work known as 'anti-spoofing', which is responsible for developing counter measures. Part of the work of biometric laboratories revolves around identifying imaginable spoofs and spoofers and developing technologies that can detect real from fake bodies. Based on fieldwork among researchers in a biometric lab and at at international conferences where policy-makers, security officials and industry discuss biometric technologies, the article shows how the figure of the spoofer epitomizes certain concerns and brings with it particular types of practices and threat scenarios. Biometric technologies, it is argued, are constantly changing shape in response to the imagined, potential threats embodied by the spoofer in, for example, state security contexts and at borders, where fears of the potential consequences of uncontrolled migration, terrorism and global crime prevail. [ABSTRACT FROM AUTHOR]