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This article investigates the performance of the Galileo Open Service Navigation Message Authentication (OSNMA) system in real-life environments prone to RF interference (RFI), jamming, and/or spoofing attacks. Considering the existing data that indicate a relatively high number of RFI- and spoofing-related incidents reported in Eastern Europe, this study details a data-collection campaign along various roads through urban, suburban, and rural settings, mostly in three border counties in East and South-East of Romania, and presents the results based on the data analysis. The key performance indicators are determined from the perspective of an end user relying only on Galileo OSNMA authenticated signals. The Galileo OSNMA signals were captured using one of the few commercially available GNSS receivers that can perform this OSNMA authentication algorithm incorporating the satellite signals. This work includes a presentation of the receiver's operation and of the authentication results obtained during test runs that experienced an unusually high number of RFI-related incidents, followed by a detailed analysis of instances when such RFI impaired or fully prevented obtaining an authenticated position, velocity, and time (PVT) solution. The results indicate that Galileo OSNMA demonstrates significant robustness against interference in real-life RF-degraded environments, dealing with both accidental and intentional interference. [ABSTRACT FROM AUTHOR]

In the rapidly evolving domain of biometric security, the significance of Fingerprint Presentation Attack Detection (FPAD) has become increasingly paramount, given the susceptibility of Automatic Fingerprint Identification System (AFIS) to advanced spoofing techniques. This systematic literature review (SLR), spanning from 2022 to the second quarter of 2024, delves into the intricate challenges and burgeoning opportunities within FPAD. It focuses on innovative methodologies for detecting presentation attacks, the prevalent challenges posed by spoof fabrications (including materials like silicone, gelatine, and latex), and the exploration of potential advancements in FPAD effectiveness. The comprehensive analysis, based on a rigorous review protocol, scrutinizes 40 seminal peer-reviewed articles from the IEEE Xplore and ScienceDirect databases. This exploration uncovers a diverse range of strategies in FPAD, including software-centric and hardware-assisted approaches, each bearing unique implications for security enhancement and user privacy considerations. A pivotal finding of this review is the identification of critical research gaps, particularly in the development of algorithms capable of universal detection, the system's adaptability to novel spoofing materials, and the ethical management of biometric data. This review provides a contemporary assessment of the current state of FPAD and establishes a foundation for future research directions. It highlights the need for continuous innovation in response to the evolving sophistication of spoofing techniques and the imperative of maintaining a balance between robust security measures and user-centric design in biometric systems. This review underscores the dynamic interplay between technological advancements, the ingenuity of attackers, and the ongoing endeavour to achieve reliable, user-friendly, and ethically responsible biometric security solutions. [ABSTRACT FROM AUTHOR]

Network slicing is a fundamental technological advancement that facilitates the provision of novel services and solutions within the realm of 5G and the forthcoming 6G communications. Numerous challenges emerge when implementing network slicing on a large-scale commercial level since it necessitates comprehensive control and automation of the entire network. Cyberattacks, such as distributed denial of service (DDoS) and address resolution protocol (ARP) spoofing, can significantly disrupt the performance and accessibility of slices inside a multi-tenant virtualized networking infrastructure due to the shared utilization of physical resources. This article employs intent-based networking (IBN) to identify and address diverse threats through automated methods. A conceptual framework is presented in which the IBN manager is integrated into the network-slicing architecture to facilitate the implementation of automated security controls. The proposed work is assessed using an experimental test bed. The study's findings indicate that the network slice's performance exhibits improvement when successful detection and mitigation measures are implemented. This improvement is observed in various metrics: availability, packet loss, response time, central processing unit (CPU) and memory utilization. [ABSTRACT FROM AUTHOR]

Indoor positioning based on Wi-Fi signals has gained a lot of attention lately. There are many advantages related to the use of Wi-Fi signals for positioning, including the availability of Wi-Fi access points in indoor environments and the integration of Wi-Fi transceivers into consumer devices. However, since Wi-Fi uses an unlicensed spectrum, anyone can create their own access points. Therefore, it is possible to affect the function of the localization system by spoofing signals from access points and thus alter positioning accuracy. Previously published works focused mainly on the evaluation of spoofing on localization systems and the detection of anomalies when updating the radio map. Spoofing mitigation solutions were proposed; however, their application to systems that use off-the-shelf items is not straightforward. In this paper filtering algorithms are proposed to minimize the impact of access point spoofing. The filtering was applied with a combination of the widely used K-Nearest Neighbours (KNN) localization algorithm and their performance is evaluated using the UJIIndoorLoc dataset. During the evaluation, the spoofing of Access Points was performed in two different scenarios and the number of spoofed access points ranged from 1 to 10. Based on the achieved results proposed SFKNN provided good detection of the spoofing and helped to reduce the mean localization error by 2–5 m, especially when the number of spoofed access points was higher. [ABSTRACT FROM AUTHOR]

The short message service (SMS) is a service for exchanging texts via mobile networks that has been developed not only as a means of text communication between subscribers but also as a means to remotely manage Internet of Things (IoT) devices. However, the originating number of an SMS can be spoofed. If IoT devices authenticate administrators based on the originating number of an SMS, the authentication is bypassed via SMS origin spoofing. Consequently, IoT devices are at risk of accepting commands from attackers and performing unauthorized actions. Accordingly, in this study, the specifications of major cellular IoT gateways were evaluated by focusing on remote management via SMS, and the authentication bypass hypothesis was verified. The results showed that 25 of the 32 targeted products supported SMS-based remote management, and 20 implemented authentication based on the originating number of the SMS. Furthermore, by spoofing the originating number of the SMS, one product was demonstrated to be remotely exploitable through authentication bypassing. Thus, this study revealed the threats posed by SMS origin spoofing to IoT devices and proved that SMS origin spoofing not only threatens text communication between people but also puts machine communication at risk. [ABSTRACT FROM AUTHOR]

The article is devoted to the identification and analysis of new vectors of cybercrime development, their localization and prevention. The analysis of secondary data, questionnaires, and expert assessment are used as research methods for this problem. The statistics of cybercrime in foreign countries and Russia in recent years have been analyzed. It is found out how the domestic business has changed its attitude to the security of information and information systems of the company. The sectoral landscape of cybercrimes in the Russian Federation for 2023 is presented. New vectors of phishing development are considered in detail and the main resources forged by hackers during phishing attacks are identified. Special attention is paid to phishing by the method of distribution, which today poses the greatest danger to Russian companies. Spoofing has been studied as a new type of cyberattack, where masquerading as a legal object (computer, device or network) is used by hackers as a means of penetrating other computer networks. The characteristics of modern types of spoofing and the negative consequences for business that they lead to are given. The measures taken at the state level to counter new vectors of cybercrime development are indicated. The conclusion is substantiated that state support and information protection measures themselves eliminate the consequences rather than the causes of high cybercrime, which is why the security problem cannot be solved without the participation of domestic business and the population of the country. The consolidated participation of Russian business in the fight against cybercrime is noted, which found its concrete embodiment in the creation of the F.C.C.T. joint-stock company. The key areas of activity of this company in the information technology market and the results of its work in 2023 are highlighted. Attention is focused on the fact that the creation of a joint-stock company F.C.C.T. in the country does not fully solve the problem of ensuring information security, which primarily concerns small and medium-sized business structures. It is stated that many of them, especially in the Russian regions, are not ready to purchase information security services not only financially, but also organizationally. Due to the low demand of entrepreneurs for vulnerability search services in the company’s software, web applications and IT infrastructure, it is proposed to build a typical SOC that meets the most minimal standards for combating cybercrime. An algorithm has been developed to achieve them in the form of step-by-step implementation of simple organizational and methodological recommendations for the prevention of cybercrimes and minimizing their consequences for small and medium-sized businesses.

In recent years, the use of high-speed algorithms has created new challenges for the integrity and stability of financial markets. One particular abusive behaviour, known as 'spoofing', has become a significant concern for regulators and market participants. Drawing on ethnographic materials, we examine the strategies behind manipulative trading practices from the perspective of market operators using algorithms. We show how spoofing is carried out in practice and discuss how the traders involved justify their actions. Our study makes two key contributions. First, we argue that the 'algorithmic war' often used to describe contemporary financial markets can be better understood as a conflict between different epistemic regimes. Second, we emphasize the need for regulators to better recognize the role of socio-technical systems in developing market abuse regulations. [ABSTRACT FROM AUTHOR]

With the expansion of wireless mobile networks into both the daily lives of individuals as well as into the widely developing market of connected devices, communication is an increasingly attractive target for attackers. As the complexity of mobile cellular systems grows and the respective countermeasures are implemented to secure data transmissions, the attacks have become increasingly sophisticated on the one hand, but at the same time the system complexity can open up expanded opportunities for security and privacy breaches. After an in-depth summary of possible entry points to attacks to mobile networks, this paper first briefly reviews the basic principles of the physical layer implementation of 4G/5G systems, then gives an overview of possible attacks from a physical layer perspective. It also provides an overview of the software frameworks and hardware tool-software defined radios currently in use for experimenting with 4G/5G mobile networks, and it discusses their basic capabilities. In the final part, the paper summarizes the currently most promising families of techniques to detect illegitimate base stations—the machine-learning-based, localization-based, and behavior-based methods. [ABSTRACT FROM AUTHOR]

The majority of local IPv6 networks continue to remain insecure and vulnerable to neighbor spoofing attacks. The Secure Neighbor Discovery (SEND) standard and its concomitant Cryptographically Generated Addressing (CGA) scheme were accepted by large standard bodies to codify practical mitigations. SEND and CGA have never seen widespread adoption due to their complexities, obscurity, costs, compatibility issues, and continued lack of mature implementations. In light of their poor adoption, research since their standardization has continued to find new perspectives and proffer new ideas. The orthodox solutions for securing Neighbor Discovery have historically struggled to successfully harmonize three core ideals: simplicity, flexibility, and privacy preservation. This research introduces Voucher-Based Addressing, a low-configuration, low-cost, and high-impact alternative to IPv6 address generation methods. It secures the Neighbor Discovery address resolution process while remaining simple, highly adaptable, indistinguishable, and privacy-focused. Applying a unique concoction of cryptographic key derivation functions, link-layer address binding, and neighbor consensus on the parameters of address generation, the resolved address bindings are verifiable without the need for complex techniques that have hindered the adoption of canonical specifications. [ABSTRACT FROM AUTHOR]

The majority of local IPv6 networks continue to remain insecure and vulnerable to neighbor spoofing attacks. The Secure Neighbor Discovery (SEND) standard and its concomitant Cryptographically Generated Addressing (CGA) scheme were accepted by large standard bodies to codify practical mitigations. SEND and CGA have never seen widespread adoption due to their complexities, obscurity, costs, compatibility issues, and continued lack of mature implementations. In light of their poor adoption, research since their standardization has continued to find new perspectives and proffer new ideas. The orthodox solutions for securing Neighbor Discovery have historically struggled to successfully harmonize three core ideals: simplicity, flexibility, and privacy preservation. This research introduces Voucher-Based Addressing, a low-configuration, low-cost, and high-impact alternative to IPv6 address generation methods. It secures the Neighbor Discovery address resolution process while remaining simple, highly adaptable, indistinguishable, and privacy-focused. Applying a unique concoction of cryptographic key derivation functions, link-layer address binding, and neighbor consensus on the parameters of address generation, the resolved address bindings are verifiable without the need for complex techniques that have hindered the adoption of canonical specifications.

In the last decade, spoofing attack has been known as the most dangerous intentional interference in Global Positioning System (GPS). This work suggested an algorithm that performs spoofing mitigation by utilising the Kalman filter. In particular, first, in the tracking stage multi-correlator structure is established and correlators output are investigated by using a Multi-Layer Perceptron Neural Network (MLP NN) in other to detect the spoofing attack, after that, the Kalman filter estimates the DLL discriminator output. The proposed method was verified using three different spoofing in which, the mitigation rate was 84.25%, 93.20% and 97.24%, respectively. [ABSTRACT FROM AUTHOR]

Fingerprint and iris traits are used in sensitive applications and so, spoofing them can impose a serious security threat as well as financial damages. Spoofing is a process of breaking biometric security using artificial biometric traits. This spoofing can be avoided by detecting the liveness of the biometric traits. Hence, liveness detection techniques have become an active research area. However, liveness detection techniques are also prone to attack because of advanced spoofing materials. Hence, they are subjected to further development to face futuristic spoofing and compromising real biometric traits. To aid the development, this paper technically and informatically reviews the state-of-the-art liveness detection techniques in the last decade. Firstly, the paper reviews the processing strata, adopted features and detectors in the existing liveness detection techniques. Secondly, the paper presents the benchmark datasets, their characteristics, availability and accessibility, along with the potential spoofing materials that have been reported in the literature under study. Thirdly, the survey reports the performance of the techniques on the benchmark datasets. Eventually, this paper summarizes the findings, gaps and limitations to facilitate strengthening of liveness detection techniques. This paper further reports that the Fingerprint Liveness Detection (FLD) techniques such as Slim-ResCNN, JLW and Jung CNN have achieved a better accuracy of 94.30%, 98.61% and 97.99%, respectively on LivDet19 datasets. It has been observed that CNN-based architectures have outperformed in significant number of FLD datasets. In contrast, Support Vector Machine (SVM) with appropriate shallow and deep features has achieved equivalent performance against deep classifiers on detecting iris spoofs from Iris Liveness Detection (ILD) datasets. [ABSTRACT FROM AUTHOR]

Increased interest in the development and integration of navigation and positioning services into a wide range of receivers makes them susceptible to a variety of security attacks such as Global Navigation Satellite Systems (GNSS) jamming and spoofing attacks. The availability of low-cost devices including software-defined radios (SDRs) provides a wide accessibility of affordable platforms that can be used to perform these attacks. Early detection of jamming and spoofing interferences is essential for mitigation and avoidance of service degradation. For these reasons, the development of efficient detection methods has become an important research topic and a number of effective methods has been reported in the literature. This survey offers the reader a comprehensive and systematic review of methods for detection of GNSS jamming and spoofing interferences. The categorization and classification of selected methods according to specific parameters and features is provided with a focus on recent advances in the field. Although many different detection methods have been reported, significant research efforts toward developing new and more efficient methods remain ongoing. These efforts are driven by the rapid development and increased number of attacks that pose high-security risks. The presented review of GNSS jamming and spoofing detection methods may be used for the selection of the most appropriate solution for specific purposes and constraints and also to provide a reference for future research. [ABSTRACT FROM AUTHOR]

This paper aims to assess the impact of Global Navigation Satellite System (GNSS) spoofing on the performance of a Garmin GPSMAP 66sr dual-frequency GNSS receiver. The evaluation is conducted through field assessments under three conditions: 1) single-frequency GPS L1 coarse acquisition (C/A) only, 2) single-frequency GPS L1 C/A and Galileo E1 open service (OS), and 3) dual-frequency GPS L1 C/A and L5, as well as Galileo E1 OS and E5a. The results emphasise the critical role of multifrequency GNSS in mitigating spoofing. In the dual-frequency multi-GNSS mode, spoofing does not occur as the GPS L5 and Galileo E5a signals remain unaffected by spoofing signals in the L1 / E1 band. In the single-frequency multi-GNSS mode, the higher number of observed GNSS satellites contributes to higher minimum spoofing power levels and longer durations between position fix loss and spoofing as compared to the GPS only mode.

This study explores the use of electronic countermeasures to disrupt communications systems in Unmanned Aerial Vehicles (UAVs), focusing on the protection of airspaces and critical infrastructures such as airports and power stations. The research aims to develop a low-cost, adaptable jamming device using Software Defined Radio (SDR) technology, targeting key UAV communication links, including geolocation, radio control, and video transmission. It applies jamming techniques that successfully disrupt UAV communications. GPS spoofing techniques were also implemented, with both static and dynamic spoofing tested to mislead the drones’ navigation systems. Dynamic spoofing, combined with no-fly zone enforcement, proved to be particularly effective in forcing drones to land or exhibit erratic behavior. The conclusions of this study highlight the effectiveness of these techniques in neutralizing unauthorized UAVs, while also identifying the need for future research in countering drones that operate on alternative frequencies, such as 4G/5G, to enhance the system’s robustness in evolving drone environments.

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The widespread use of iris recognition systems has led to a growing demand for enhanced security measures to counter potential iris presentation attacks, also known as anti-spoofing. To enhance the security and reliability of iris recognition systems, researchers have developed numerous methods for detecting presentation attacks. Most of these methods lack precision in detecting unknown attacks compared to known attacks. In addition, most literature on iris presentation attack detection (PAD) systems utilizes near-infrared (NIR) samples as inputs. These samples produce superior-quality and robust images with less reflection in the cornea of the eye. Despite this, due to the widespread use of smartphones and the necessity for unsupervised identity verification, visible-light samples play a crucial role in detecting presentation attacks. These samples can be easily captured using smartphone cameras. In this paper, a dual-spectral attention model has been developed to train a unified model for multiple real-world attack scenarios. Two different scenarios were tested. In the first scenario, the model was trained as a one-class anomaly detection (AD) approach, while in the second scenario, it was trained as a normal two-class detection approach. This model achieved the best result for the attack presentation classification error rate (APCER) of 4.87% in a one-class AD scenario when tested on the attack dataset, outperforming most studies on the same test dataset. These experimental results suggest that future research opportunities in areas such as working with visible light images, using an AD approach, and focusing on uncontrolled environment samples and synthetic iris images may improve iris detection accuracy [ABSTRACT FROM AUTHOR]

Location and navigation information has become an important part of our lives. GPS data is vital in many areas from transportation to economy, from energy to security. The alteration of this information can have deleterious downstream effects that cannot be reversed. Today, it is possible to attack GPS data with accessible hardware and open source software. In this paper, the signal obtained with the help of a transceiver that samples the high frequency RF signals down to the intermediate frequency was compared with the RMS value by windowing over PSD. Then this data was statistically analyzed with the NIST runs test. In order to realize GPS signal spoofing in a short time that after GPS receiver lock the spoofing signal, field programmable gates connected to the transceiver were used. The results obtained varied according to the dimensions of the windowing process, and it was understood that the window size was in a correlation with the detection of deception in a short time. The suggested method's key benefit is its simplicity, which makes it adaptable to various GNSS systems. [ABSTRACT FROM AUTHOR]

The overlapping disasters of the Australian 2019–2020 bushfire season and the COVID-19 pandemic, figured alongside the imaginary of projected future disasters, have provided a space of legitimation to experiment with controversial facial recognition technologies (FRTs). Drawing upon interviews conducted with senior Australian government administrators and researchers, I argue that FRTs are being used to respond to the trauma of disaster through its novel mediation and refiguration, tied to discourses of resilience which have been used to justify the expansion of FRT as a means for relief and the provision of aid. This legitimation, however, is challenged by the difficulty FRT encounters in capturing the face in its vital and its mortal malleability. What I term 'the problem of the corpse' serves to bring to light the 'paranoid' gaze of the biometric apparatus, disrupting the aim of using biometric infrastructure to produce a 'new normal' in the ongoing aftermath of disaster. [ABSTRACT FROM AUTHOR]

Biometric data (facial, voice, fingerprint, and retinal scans, for example) are widely used in identification due to their unique and irreversible nature. Facial recognition technologies are employed in a wide range of applications due to their contactless nature and convenience. However, technological advancements and the availability of access to personal information have rendered these biometric systems susceptible to attacks utilizing fake faces. As a result, the issue of anti-spoofing has emerged as a critical one in the field of facial recognition. This study proposes a joint face presentation attack (FPA) detection method based on face-weighted multi-color multi-level LBP features extracted from the combination of device-dependent HSV and device-independent L*a*b* color spaces. The facial images were converted to HSV and L*a*b* color spaces. Three levels of regional LBP features were extracted from each color channel and then concatenated. Finally, a Multi-Color Multi-Level LBP (MCML_LBP) feature vector was obtained. In addition, the Face Weighted MCML_LBP feature vector was produced (FW_MCML_LBP) by adding the LBP histogram extracted from the central region of the normalized image. The feature vectors are used to train an SVM classifier after reducing their size using PCA. Twenty-five different test scenarios were subjected to experimentation on the CASIA and Replay-Attack databases. 2.11% EER and 0.19% HTER were achieved on CASIA (Overall) and Replay-Attack (Grandtest) databases, respectively, using the L*a*b color space and the proposed feature extraction method. The results of the study showed that the proposed method was successful in FPA detection compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The short message service (SMS), a service for exchanging texts via mobile networks, has become a universal means of communication. SMS is a mechanism for sending a text message to a specific mobile phone number. Its protocol enables the sender to specify the destination phone number but not the originating number. However, the short message peer-to-peer (SMPP) protocol, which forwards SMS messages, enables the sender to specify the originating number. If the SMPP protocol allows the sender to specify another person's phone number as the originator, an attacker can use it to send a short message impersonating another person. In this study, short messages were sent from an SMPP gateway service with any phone number specified as the originator, and their delivery to the Japanese mobile network was verified. All the verified mobile network operators (MNOs) were able to spoof the origin to any international phone number, and one MNO was able to spoof it to any national phone number. The results of this study reveal that SMPP can potentially be exploited to spoof the originating number of an SMS. Furthermore, a threat scenario was devised using spoofing and mitigation measures. [ABSTRACT FROM AUTHOR]

This paper aims to assess the impact of Global Navigation Satellite System (GNSS) spoofing on the performance of a Garmin GPSMAP 66sr dualfrequency GNSS receiver. The evaluation is conducted through field assessments under three conditions: 1) single-frequency GPS L1 coarse acquisition (C/A) only, 2) single-frequency GPS L1 C/A and Galileo E1 open service (OS), and 3) dual-frequency GPS L1 C/A and L5, as well as Galileo E1 OS and E5a. The results emphasise the critical role of multifrequency GNSS in mitigating spoofing. In the dual-frequency multi-GNSS mode, spoofing does not occur as the GPS L5 and Galileo E5a signals remain unaffected by spoofing signals in the L1 / E1 band. In the single-frequency multi-GNSS mode, the higher number of observed GNSS satellites contributes to higher minimum spoofing power levels and longer durations between position fix loss and spoofing as compared to the GPS only mode. [ABSTRACT FROM AUTHOR]

With the rapid evolution of attack techniques and attacker targets, companies and researchers question the applicability and effectiveness of security taxonomies. Although the attack taxonomies allow us to propose a classification scheme, they are easily rendered useless by the generation of new attacks. Due to its distributed and open nature, web services give rise to new security challenges. The purpose of this study is to apply a methodology for categorizing and updating attacks prior to the continuous creation and evolution of new attack schemes on web services. Also, in this research, we collected thirty-three (33) types of attacks classified into five (5) categories, such as brute force, spoofing, flooding, denial-of-services, and injection attacks, in order to obtain the state of the art of vulnerabilities against web services. Finally, the attack taxonomy is applied to a web service, modeling through attack trees. The use of this methodology allows us to prevent future attacks applied to many technologies, not only web services.

The emergence of small-drone technology has revolutionized the way we use drones. Small drones leverage the Internet of Things (IoT) to provide precise navigation and location-based services, making them versatile tools for various applications. However, small drones’ structural and design vulnerabilities expose them to significant security and privacy threats. To ensure the secure and reliable operation of small drones, developing a robust network infrastructure and implementing tailored security and privacy mechanisms is essential. The research evaluates the performance of deep learning (DL) models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and ConvLSTM, in detecting intrusions within UAV communication networks. The study utilizes five diverse and realistic datasets, namely KDD Cup-99, NSL-KDD, WSN-DS, CICIDS2017, and Drone datasets, to simulate real-world intrusion scenarios. Notably, the ConvLSTM model consistently achieves an accuracy of 99.99%, showcasing its potential in securing UAVs from cyber threats. This research underscores the significance of robust cybersecurity measures in the ever-expanding realm of UAV technology and highlights the pivotal role played by high-quality datasets in enhancing UAV security. As UAVs become increasingly integral to various industries, this study contributes to ensuring their safety, security, and reliability in the face of evolving cyber risks.

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.

The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector. [ABSTRACT FROM AUTHOR]

Spoofing—placing orders on financial exchanges intending to withdraw them prior to execution—is widely legally prohibited. I argue instead on two main grounds that spoofing should be permitted and legalised. The first is that spoofing as a form of bluffing remains within the market practice of making legally binding offers—as opposed to lying or betraying trust—and primarily concerns the spoofer's personal information. As a form of bluffing spoofing helps prevent financial speculators, in particular high-frequency algorithmic traders, from easily profiting by other market actors reliably revealing their underlying preferences through their market activity. The second is that at the systemic level permitting spoofing would benefit non-speculative actors who place orders to hedge economic risk and whose activities provide the raison d'être for financial exchanges, differentiating them from simple forums for gambling. I also address potential concerns that legalised spoofing would drive speculators out of financial markets entirely and, therefore, undermine market liquidity. This work contributes to the wider debate in business ethics regarding bluffing by illustrating the acceptability of bluffs which do not betray counterparty trust or reliance on testimony by remaining within the framework of market practices. [ABSTRACT FROM AUTHOR]

Who could have guessed 120 years ago, when electronic warfare (EW) was accidentally discovered, that it would have such a big impact on battlefield? For almost 30 years, after the cold war ended, electronic warfare was forgotten, but the war from Ukraine, revived it again. What’s in an electronic warfare system? It is an integrated, autonomous, portable or vehicle mounted electronic system, whether configured to attack, protect or support, must have a way to collect and make sense of the signals in its environment. Electronic warfare systems can be configured for a variety of different missions and use a lot of different subsystems. It must identify what’s out there, understand how it is using the spectrum, and determining if it is a threat. The system must then neutralize it and passes the threat data to the technique generator which determines how the system should respond to address the threat. For an electronic system to conduct electronic attack or electronic protect missions, it must be able to broadcast signals of its own to dominate the electromagnetic spectrum. Once a threat is analyzed, and a response generated, it is the ability of the EW system’s transmitters to precisely radiate electromagnetic energy that makes jamming, spoofing, deception and other electronic activities. [ABSTRACT FROM AUTHOR]

This paper proposes a method of countering spoofing attacks by improving the resilience of face-based biometric authentication systems to digital face manipulation attacks on the biometric input module. The proposed method of digital face manipulation detection (deepfake detection) is based on a convolutional neural network trained on a large dataset containing various types of manipulations, images of different quality, and a large number of identities and as a result achieves an accuracy of at least 99%. Experiment results also indicate high performance of the proposed approach compared to other available methods tested on the same dataset. The method can be used to improve the security of biometric authentication systems by reducing the risk of unauthorized access. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]