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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.

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]

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]

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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]

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]

Copyright of Revista Opinião Jurídica is the property of Revista Opiniao Juridica 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.)

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.

The increasing use of Global Positioning System (GPS)-based autonomous drones in various civilian and military applications has raised concerns about malicious or unintentionally harmful activities that can be carried through them. It is necessary to detect these intruding drones within protected areas and prevent their unauthorized access by denying them entry. We propose a framework that combines the detection of intruding drones using an L-band radar and then counters by transmitting fake GPS coordinates toward the drones, effectively redirecting them. This article explains the setup required to add to an existing monostatic radar that provides two-dimensional information, i.e., range and azimuth information, to enable the proposed setup to get the elevation angle of the drone. We propose a linear array design using digital receive-only beamforming techniques in the elevation domain to compute the elevation angle in addition to the range, velocity, and azimuth information being provided by the monostatic radar to get complete information about the intruding drone. The simulation of drone detection is followed by an examination of the impact of transmitting fabricated GPS coordinates to the drone. Experimental verification has been conducted to validate both the digital beamforming algorithm and the spoofing technique. This approach blocks the reception of actual GPS signals in the drone and replaces the drone's GPS coordinates with alternative, desired coordinates. The proposed framework can be used to prevent unauthorized drone intrusions in the protected area. [ABSTRACT FROM AUTHOR]

The aim of this paper was to propose a method to characterize the ability of a GNSS user to detect a spoofing attack from the behavior of the clock bias. Spoofing interference is not a new issue, especially in military GNSS, although it is a new challenge for civil GNSS, since it is currently implemented and used in many everyday applications. For this reason, it is still a topical issue, especially for receivers that only have access to high-level data (PVT,CN0). To address this important issue, after conducting a study of the receiver clock polarization calculation process, this led to the development of a very basic Matlab model that emulates a spoofing attack at the computational level. Using this model, we were able to observe that the clock bias is affected by the attack. However, the amplitude of this disturbance depends on two factors: the distance between the spoofer and the target and the synchronization between the clock that generates the spoofing signal and the reference clock of the constellation. To validate this observation, more or less synchronized spoofing attacks were carried out on a fixed commercial GNSS receiver with the use of GNSS signal simulators and also with a moving target. We propose then a method to characterize the capacity of detecting a spoofing attack with the clock bias behavior. We present the application of this method for two commercial receivers of the same manufacturer from different generations.

This article discusses the increasing security risk for the Global Navigation Satellite System (GNSS) due to both unintentional and deliberate interference (attacks), which have gotten significantly worse in 2022 due to tense the international situation. The upcoming Galileo Public Regulated Service (PRS), which is more resilient and robust than initial GNSS open services, is one of the key solutions for that problem. The technical description of this service, aspects regarding its implementation in the EU and the role of designated governmental authorities in that process are extensively covered in the first sections of the article. The next relevant issue brought up in the paper is the PRS signals’ coexistence with amateur services operating within the same frequency resources, which have recently became a source of significant controversy in Europe. Finally, the article presents the Polish contribution to the Galileo PRS preparatory actions, covering the participation in two international R&D projects, the developed measurement station and initial results for the GNSS receiver’s jamming and spoofing resistance tests, as well as the concept of the Galileo PRS threats detection system.

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]

• 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]

This work studies the feasibility of using backscatter-modulated tags to introduce false information into a signal received by a frequency-modulated continuous-wave (FMCW) radar. A proof-of-concept spoofing device was designed in the 24 GHz ISM band. The spoofing device was based on an amplifier connected between two antennas, and modulation was carried out by switching the amplifier bias. The use of an amplifier allowed us to increase the level of spoofing signal compared with other modulated backscattering methods. The simulated and experimental results show that our method has the ability to generate a pair of false targets at different ranges and velocities depending on the modulation frequency of the chosen tag, since sidebands appear due to this modulation. Countermeasures to detect the spoofing attack based on changes in the slope of the frequency sweep between frames are also proposed. [ABSTRACT FROM AUTHOR]

The civilian Automatic Identification System (AIS) has no inherent protection against spoofing. Spoofed AIS messages have the potential to interfere with the safe navigation of a vessel by, amongst other approaches, spoofing maritime virtual aids to navigation and/or differential global navigation satellite system (DGNSS) correction data conveyed across it. Acting maliciously, a single transmitter may spoof thousands of AIS messages per minute with the potential to cause considerable nuisance; compromising information provided by AIS intended to enhance the mariner's situational awareness. This work describes an approach to authenticate AIS messages using public key cryptography (PKC) and thus provide unequivocal evidence that AIS messages originate from genuine sources and so can be trusted. Improvements to the proposed AIS authentication scheme are identified which address a security weakness and help avoid false positives to spoofing caused by changes to message syntax. A channel loading investigation concludes that sufficient bandwidth is available to routinely authenticate all AIS messages whilst retaining backwards compatibility by carrying PKC 'digital signatures' in a separate VHF Data Exchange System (VDES) side channel. [ABSTRACT FROM AUTHOR]

Audio classification in smart devices is complex in comparison to image or text classification and recognition as it requires to clean the audio or pre‐process it before extracting its features and thus the accuracy heavily depends upon the pre‐processing of the audio. In this paper, we have used an envelope function having a specific threshold that removes the useless part of the audio which cannot be used to differentiate one audio from another by the smart device. Further, pre‐processed sounds from different musical instruments are subjected to feature extraction and classification using deep convolutional neural networks and Recurrent Neural Networks. It is observed that our proposed approach is suitable for audio classification for smart devices, with results at par with state‐of‐the‐art techniques. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Spoofing (canonically: 'bidding or offering with the intent to cancel the bid or offer before execution'), once a valued skill in face-to-face trading, has become a crime punishable by jail. Echoing Riles's call for greater attention to law in research on finance, this paper analyses the interwoven processes of this dramatic shift, including trading's changing material form, contingencies such as the Congressional response to the global financial crisis, and, above all, the use of criminal (not just civil, administrative) law. Criminal law's particularly strong boundary work – specifically the first criminal indictment and jail sentence for spoofing – rendered earlier ambivalent attitudes and inconsistent enforcement untenable. Nevertheless, drawing a boundary between spoofing and legitimate trading remains work-in-progress, with simultaneously legal, material and moral dimensions. [ABSTRACT FROM AUTHOR]

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.

In recent years it has been repeatedly discussed, even on the pages of this Magazine, of the dependence of our society from space systems and attacks, more or less evident, worked against them in various forms. Particularly worrying, for whom deals with geomatics, were the various and often repeated episodes of "jamming" and "spoofing" towards signals GNSS, especially GPS, in various areas of the world, such as the Middle East, Strait of Hormuz, China Sea, Korea. No less serious, though perhaps less well known, the attempts at attacks cybernetic versus satellite systems for Earth observation and the communications. [ABSTRACT FROM AUTHOR]

Biometric systems identify individuals based on unique traits such as the face, fingerprints, iris etc. The main objective of the study is to understand the role of deep learning in the process of authentication as well as its application in the enhancement of security of biometric systems. We highlight the studies using deep learning approaches to authenticate enrolled users under ideal and non-ideal environmental conditions. We summarize these approaches and explore the challenges that continue to restrict the full potential of biometric systems. The foremost are: building robust algorithms for authentication, ensuring the security of enrolled templates and protecting systems against spoofing attacks. In this paper, we review the performance achieved by various studies in overcoming the aforesaid challenges, along with the potential improvements and future directions in this domain. [ABSTRACT FROM AUTHOR]

Observation of terrestrial GNSS interference (jamming and spoofing) from low Earth orbit (LEO) is a uniquely effective technique for characterizing the scope, strength, and structure of interference and for estimating transmitter locations. Such details are useful for situational awareness, interference deterrence, and the development of interference‐hardened GNSS receivers. This paper presents the results of a three‐year study of global interference, with emphasis on a particularly powerful interference source active in Syria since 2017. It then explores the implications of such interference for GNSS receiver operation and design. [ABSTRACT FROM AUTHOR]

This article presents a novel spoofing device capable of injecting false target information into a frequency-modulated continuous-wave (FMCW) radar. The spoofing device uses a radio frequency (RF) single-sideband (SSB) mixer to introduce a frequency shift to the incoming RF signal transmitted by the victim radar and retransmits the modulated RF signal. The modulated RF signal resembles a false target. Upon down-conversion on the receiver chain of the victim radar, the modulated RF signal creates an illusion of a real target in the radar signal processing system. The frequency shift can be adjusted to vary the range of the spoofed target. The theory of the spoofing mechanism was developed, and a 5.8 GHz prototype was built for experimental validation. Experimental results demonstrate the ability of the proposed spoofing device to inject a false target at any arbitrary range. A hybrid-chirp FMCW approach was proposed and verified as a countermeasure to distinguish a real target from a spoofed target to mitigate the RF-spoofing attack. [ABSTRACT FROM AUTHOR]

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.

Abstract 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 Cramér-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.