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This article presents a method for detecting and mitigating intermediate GNSS spoofing. In this type of attack, at its early stage, a spoofer transmits counterfeit signals which have slight time offsets compared to true signals arriving from satellites. The anti-spoofing method proposed in this article fuses antenna array processing techniques with a multipath detection algorithm. The latter is necessary to separate highly correlated true and counterfeit GNSS signals. Spoofing detection is based on comparison of steering vectors related to received spatial components. Whereas mitigation is achieved by means of adaptive beamforming which excises interferences arriving from common direction and preserves undistorted signals from GNSS satellites. Performance of proposed method is evaluated through simulations, results of which prove the usefulness of this method for protecting GNSS receivers from intermediate spoofing interference.

With the advent of automated speaker verification (ASV) systems comes an equal and opposite development: malicious actors may seek to use voice spoofing attacks to fool those same systems. Various counter measures have been proposed to detect these spoofing attacks, but current offerings in this arena fall short of a unified and generalized approach applicable in real-world scenarios. For this reason, defensive measures for ASV systems produced in the last 6-7 years need to be classified, and qualitative and quantitative comparisons of state-of-the-art (SOTA) counter measures should be performed to assess the effectiveness of these systems against real-world attacks. Hence, in this work, we conduct a review of the literature on spoofing detection using hand-crafted features, deep learning, and end-to-end spoofing countermeasure solutions to detect logical access attacks, such as speech synthesis and voice conversion, and physical access attacks, i.e., replay attacks. Additionally, we review integrated and unified solutions to voice spoofing evaluation and speaker verification, and adversarial and anti-forensic attacks on both voice counter measures and ASV systems. In an extensive experimental analysis, the limitations and challenges of existing spoofing counter measures are presented, the performance of these counter measures on several datasets is reported, and cross-corpus evaluations are performed, something that is nearly absent in the existing literature, in order to assess the generalizability of existing solutions. For the experiments, we employ the ASVspoof2019, ASVspoof2021, and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifiers. For reproducibility of the results, the code of the testbed can be found at our GitHub Repository (https://github.com/smileslab/Comparative-Analysis-Voice-Spoofing).

To ensure that unmanned aerial vehicle (UAV) positioning is not affected by GPS spoofing signals, we propose PerDet, a perception-data-based UAV GPS spoofing detection approach utilizing machine learning algorithms. Based on the principle of the position estimation process and attitude estimation process, we choose the data gathered by the accelerometer, gyroscope, magnetometer, GPS and barometer as features. Although these sensors have different shortcomings, their variety makes sure that the selected perception data can compensate for each other. We collect the experimental data through real flights, which make PerDet more practical. Furthermore, we run various machine learning algorithms on our dataset and select the most effective classifier as the detector. Through the performance evaluation and comparison, we demonstrate that PerDet is better than existing methods and is an effective method with a detecting rate of 99.69%. For a fair comparison, we reproduce the existing method and run it on our dataset to compare the performance between this method and our PerDet approach.

Global Navigation Satellite System (GNSS) is pervasively used in position, navigation, and timing (PNT) applications. As a consequence, important assets have become vulnerable to intentional attacks on GNSS, where of particular relevance is spoofing transmissions that aim at superseding legitimate signals with forged ones in order to control a receiver’s PNT computations. Detecting such attacks is therefore crucial, and this article proposes to employ an algorithm based on deep learning to achieve the task. A data-driven classifier is considered that has two components: a deep learning model that leverages parallelization to reduce its computational complexity and a clustering algorithm that estimates the number and parameters of the spoofing signals. Based on the experimental results, it can be concluded that the proposed scheme exhibits superior performance compared to the existing solutions, especially under moderate-to-high signal-to-noise ratios.

UAVs are widely used in agriculture, the military, and industry. However, it is easy to perform GPS spoofing attacks on UAVs, which can lead to catastrophic consequences. In this paper, we propose ConstDet, a control semantics-based detection approach for GPS spoofing attacks of UAVs using machine learning algorithms. Various real experiments are conducted to collect real flight data, on the basis of which ConstDet is designed as a practical detection framework. To train models for the detection of GPS spoofing attacks, specified flight data types are selected as features based on the control semantics, including the altitude control process and the horizontal position control process, since these data are able to represent the dynamic flight and control processes. Multiple machine learning algorithms are used to train and generate the best classifier for GPS spoofing attacks. ConstDet is further implemented and deployed on a real UAV to support onboard detection. Experiments and evaluations validate that ConstDet can effectively detect GPS spoofing attacks and the detection rate can reach 97.70%. The experimental comparison demonstrates that ConstDet has better performance than existing detection approaches.

The global positioning system (GPS) is an essential technology that provides positioning capabilities and is used in various applications such as navigation, surveying, mapping, robot simultaneous localization and mapping (SLAM), location-based service (LBS), etc. However, the GPS is known to be vulnerable to intentional attacks such as spoofing because of its simple signal structure. In this study, a direct method is proposed for GPS spoofing detection, using Attitude and Heading Reference System (AHRS) accelerometer and analyzing the detection performance with corresponding probability density functions (PDFs). The difference in the acceleration between the GPS receiver and the accelerometer is used to detect spoofing. The magnitude of the acceleration error may be used as a decision variable. Additionally, using the magnitude of the north (or east) component of the acceleration error as another decision variable is proposed, which shows better performance in some conditions. The performance of the two decision variables is compared by calculating the probability of spoofing detection and the detectable minimum spoofing acceleration (DMSA), given a pre-defined false alarm probability and a pre-defined detection probability. It turns out that both decision variables need to be used together to obtain the best spoofing detection performance.

Jamming and spoofing are the two main types of intentional interference for global navigation satellite system (GNSS) receivers. Due to the entirely different signal characteristics they have, a few techniques can deal with them simultaneously. This paper proposes a two-stage interference suppression scheme based on antenna arrays, which can detect and mitigate jamming and spoofing before the despreading of GNSS receivers. First, a subspace projection was adopted to eliminate the high-power jamming signals. The output signal is still a multi-dimensional vector so that the spatial processing technique can be used in the next stage. Then, the cyclostationarity of GNSS signals were fully excavated to reduce or even remove the noise component in the spatial correlation matrix. Thus, the signal subspace, including information of the power and the directions-of-arrival (DOAs) of the GNSS signals, can be obtained. Next, a novel cyclic correlation eigenvalue test (CCET) algorithm was proposed to detect the presence of a spoofing attack, and the cyclic music signal classification (Cyclic MUSIC) algorithm was employed to estimate the DOAs of all the navigation signals. Finally, this study employed a subspace projection again to eliminate the spoofing signals and provide a higher gain for authentic satellite signals through beamforming. All the operations were performed on the raw digital baseband signal so that they did not introduce additional computational complexity to the GNSS receiver. The simulation results show that the proposed scheme not only suppresses jamming and spoofing effectively but also maximizes the power of the authentic signals. Nonetheless, the estimated DOA of spoofing signals may be helpful for the interference source positioning in some applications.

Spoofing attacks are one of the severest threats for global navigation satellite systems (GNSSs). This kind of attack can damage the navigation systems of unmanned air vehicles (UAVs) and other unmanned vehicles (UVs), which are highly dependent on GNSSs. A novel method for GNSS spoofing detection based on a coupled visual/inertial/GNSS positioning algorithm is proposed in this paper. Visual inertial odometry (VIO) has high accuracy for state estimation in the short term and is a good supplement for GNSSs. Coupled VIO/GNSS navigation systems are, unfortunately, also vulnerable when the GNSS is subject to spoofing attacks. The method proposed in this article involves monitoring the deviation between the VIO and GNSS under an optimization framework. A modified Chi-square test triggers the spoofing alarm when the detection factors become abnormal. After spoofing detection, the optimal estimation algorithm is modified to prevent it being deceived by the spoofed GNSS data and to enable it to carry on positioning. The performance of the proposed spoofing detection method is evaluated through a real-world visual/inertial/GNSS dataset and a real GNSS spoofing attack experiment. The results indicate that the proposed method works well even when the deviation caused by spoofing is small, which proves the efficiency of the method.

In this paper, we present an algorithmic framework for signal‐geometry‐based approaches of GNSS spoofing detection. We formulate a simple vs. simple hypothesis test independent of nuisance parameters that results in significantly reduced missed detection probability compared to prior approaches. It is highly tractable such that it can be computed online by the receiver. We employ a hypothesis iteration framework that finds spoofed subsets of satellites efficiently and accounts for the presence of weak multipath, for a provable decision behavior in safety‐of‐life applications. We support the theoretical derivations by showing results on previously published simulated and on‐air data sets. We validate the measurement model and show robustness to multipath with flight data from a Dual Polarization Antenna (DPA) mounted on a C12 aircraft. Finally, we show the algorithm's benefit on data recorded during a government‐sponsored live spoofing event.

Spoofing signal detection is an essential process in GNSS anti-spoofing. The detection probability of the existing metrics degrades significantly for many specific combinations of relative code phases and carrier phases of the spoofing signal relative to the authentic signal. To improve detection, we propose a four-complex-correlator metric, called weighted double ratio (WDR), which first constructs a metric named RatioQ exploiting the quadra-phase correlation outputs and then combines two pairs of complex correlators separated by a large correlator spacing and weighted according to their noise levels to form a WDR. Theoretical analysis and simulation results show that the detection coverage rate, the receiver operating characteristic, and the detection probability versus different carrier to noise ratios are significantly improved. The experiment using the Texas spoofing test battery data in all eight cases demonstrates that the proposed method outperforms the existing metrics, especially for high spoofing-signal-ratios and the unlocked-frequency mode.