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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]

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]

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.

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.

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]

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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]

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.

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]

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]

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]

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.

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 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]

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]

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]

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]

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.

One of the main challenges for ticketing in Mobility as a Service is the integration of the public and individual transport modes into a unified ticketing service. To realize this concept, a trustworthy identification of transport modes that is resilient to possible attacks is required. In this work, we propose two smartphone-based methods to seamlessly identify the use of trams, buses, subways, walking and bicycles, which are able to detect GNSS-based attacks and continue to provide a trustworthy identification of transport modes. We have recorded real-world measurements with commercial smartphones using the transport network in Munich and Paris. Our results show that it is possible to provide trustworthy identification of transport modes even when the system is under attack. In conclusion, in this work we demonstrate the vulnerability of smartphone-based ticketing to GNSS-based attacks, propose two methods to overcome this vulnerability and demonstrate the validity of our methods with real-world measurements.

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. [ABSTRACT FROM AUTHOR]

NovAtel has recently leveraged its expertise in both receiver design and anti-jam technology to develop solutions for space- and weight-constrained applications in challenged GNSS environments. Robust Dual-Antenna Receiver (RoDAR), is based on a commercial dual-antenna receiver, originally designed for attitude determination, and employs special firmware to mitigate jammers and spoofers without an increase in size or power consumption. With RoDAR, the multi-frequency, multi-constellation dual-antenna receiver is capable of null-steering at two different frequency bands (e.g., L1 and L5). In September 2022, the Norwegian Public Roads Administration hosted JammerTest, a live, over-the-air broadcast jamming and spoofing test. This paper presents the jamming and spoofing detection and mitigation performance of RoDAR during this live broadcast test. The interference detection provides spectrum monitoring and jamming characterization on all GNSS bands. The mitigation is carried out by steering a null formed on-board the receiver towards a jamming/spoofing source at GPS L1 and L5 bands. The null steering performance is characterized as a function of signal and position availability compared to a non-protected NovAtel receiver. The effectiveness of the anti-jam and anti-spoofing technology is demonstrated using representative complex spoofing and jamming test cases during this event. [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]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Open System Interconnection (OSI) model, consisting of seven layers, has become increasingly important in addressing cyber security issues. The rapid growth of network technology has led to a rise in cyber threats, with botnets taking over fixed and mobile computers. The widespread availability of mobile devices has led to increased app consumption, with 60% of Android malware containing major or minor botnets. The ease of accessibility of mobile devices has accelerated the adoption of mobile apps in various use cases. This article aims to identify and reduce botnets in operating systems, focusing on identifying them faster and reducing attack impact. The study analyzes botnet characteristics under controlled conditions and creates four traffic flow components across multiple time ranges. Using machine learning, flow vectors are created to identify internet flows as botnet flows or predicted flows. The method uses a combination of Boosted decision tree ensemble classifier, Naive Bayesian statistical classifier, and SVM discriminative classifier to accurately identify both well-known and novel botnets, reducing false positives and improving detection accuracy. [ABSTRACT FROM AUTHOR]

The relevance of the research is associated with the rapid development of the Internet and information technologies. This fact has greatly simplified the ability to access a variety of information resources. Along with the huge positive impact of this phenomenon on the development of modern society, serious problems have arisen related to the unfair use of information [6]. In particular, this is the illegal attribution of authorship to another's work or incorrect borrowing of a part of it (the so-called plagiarism problem) [12]. This phenomenon is especially widespread among students. Among students such a method of writing works as copying materials from the Internet with minimal editing is common. Currently, teachers are actively using online services to verify the uniqueness of the work. These are text borrow detection systems that easily block work due to low originality. To increase the “originality” of the work, some students use the anti-plagiarism-killer program (a program to hide the detection of text borrowings). There are services offering to make the text 75-99 per cent “original” [4]. A search for information on this issue revealed a lack of modern specialized literature and relevant knowledge among university teachers. Thus, the development of modern technologies for detecting traces of programs to hide the detection of text borrowings seems to be very relevant. The purpose of the research is to identify the capabilities of the anti-plagiarism-killer program to level the operation of the “Anti-plagiarism.vuz” system, to develop a technology for identifying jobs that have been processed by the anti-plagiarism killer program. The research methods used are analysis of the problem situation, methods of the theory of information retrieval, information processing and pattern recognition. The results and key findings: the article describes the most common technical methods of circumventing the anti-plagiarism system and methods for their detection. The material was prepared on the example of the popular text editor Microsoft Word and the most popular online anti-plagiarism. Vuz. service. Key conclusions about the imperfection of the anti-plagiarism.vuz online service have been made, the need for its further improvement and the warning against the use of anti-plagiarism-killer programs have been noted. The need has been identified to constantly improve information culture and deepen knowledge in the field of information technology by university teachers.

Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, and standards). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices. The dataset is available on the CIC Dataset website. [ABSTRACT FROM AUTHOR]

Objectives: In view of the difficulty of quantitative evaluation caused by complex spoofing scenarios and numerous anti-spoofing strategies, it is necessary to form effective indicators and methods for the evaluation of spoofing countermeasures. Methods: The evaluation model and mapping process from spoofing scenes to spoofing countermeasure effect are established. Based on the power space in the spoofing scenes and the spoofing effective probability in the spoofing countermeasure effect, the evaluation method and index based on the power feasible region are designed. Results: Through simulation, the quantitative evaluation results based on power feasible region of two spoofing countermeasure strategies are given, and the simulation results show that the evaluation method can realize the quantitative comparison of the performance of different anti-spoofing strategies. Conclusions: The indicator of the power feasible region can provide a quantitative evaluation method forselection and optimization of the anti-spoofing algorithms in receivers. [ABSTRACT FROM AUTHOR]