Showing 7 results for Spoofing
M. R. Mosavi, Z. Shokhmzan,
Volume 11, Issue 3 (9-2015)
Abstract
The Global Positioning System (GPS) signals are very weak signal over wireless channels, so they are vulnerable to in-band interferences. Therefore, even a low-power interference can easily spoof GPS receivers. Among the variety of GPS signal interference, spoofing is considered as the most dangerous intentional interference. The spoofing effects can mitigate with an appropriate strategy in the receiver. In this paper, we use methods of adaptive filter based on Least Mean Squares (LMS) and Normalized Least Mean Squares (NLMS) algorithms in-order to defense against spoofing. The proposed techniques are applied in the acquisition stage of the receiver. The proposed methods have been implemented on real dataset. The results explain that the suggested algorithms significantly decrease spoofing. Also, they improve Position Dilution of Precision (PDOP) parameter. Based on the results, NLMS algorithm has better performance than LMS algorithm.
P. Teymouri, M. R. Mosavi, M. Moazedi,
Volume 14, Issue 3 (9-2018)
Abstract
Due to widespread use of Global Positioning System (GPS) in different applications, the issue of GPS signal interference cancelation is becoming an increasing concern. One of the most important intentional interferences is spoofing signals. An effective interference (delay spoof) reduction method based on adaptive filtering is developed in this paper. The principle of method is using adaptive filters to eliminate interference, obtain an estimate of interfering signal and subtract that from the corrupted signal. So, what remains in the output is the desired signal. Here, for updating the filter coefficients adaptive algorithms in both time (statistical and deterministic) and transform domain will be studied. The proposed adaptive filter is applied to a batch of spoofing GPS data in pseudo-range level. The results indicate that all investigated algorithms are able to reduce positioning steady-state miss-adjustment up to 70 percent. In this context, the variable step-size least mean square algorithm performs better than others do.
K. Zarrinnegar, S. Tohidi, M. R. Mosavi, A. Sadr, D. M. de Andrés,
Volume 19, Issue 1 (3-2023)
Abstract
The Global Positioning System (GPS) is vulnerable to various deliberate and unintentional interferences. Therefore, identifying and coping with various interferences in this system is essential. This paper analyzes a method of reducing the dimensions of Cross Ambiguity Function (CAF) images in improving the identification of spoofing interference at the GPS using Multi-Layer Perceptron Neural Network (MLP NN) and Convolutional Neural Network (CNN). Using the proposed method reduces data complexity, which can reduce the number of learning data requirements. The simulation results indicate that, by applying the proposed image processing algorithm for different dimensions of CAF images, the CNN performs better than MLP NN in terms of training accuracy; the MLP NN is superior to CNN in terms of convergence speed of training. In addition, the results demonstrate that the operation of the proposed method is appropriate in the case of small-delay spoofed signals. Therefore, for the intervals above 0.25 code chip, the proposed method detects spoofing attacks with a correct detection probability close to one.
M. J. Jahantab, S. Tohidi, Mohammad Reza Mosavi, Ahmad Ayatollahi,
Volume 20, Issue 4 (11-2024)
Abstract
Global Positioning System (GPS)-based positioning has become an indispensable part of our daily lives. A GPS receiver calculates its distance from a satellite by measuring the signal reception delay. Then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. There is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the GPS receiver. In this paper, spoofing detection based on the Cross Ambiguity Function (CAF) is used. Furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. The reduced-dimensional images are trained by a Convolutional Neural Network (CNN). Additionally, a modified CNN model as Transformed-CNN (TCNN) is presented to enhance accuracy in this paper. The simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.
M. J. Jahantab, S. Tohidi, Mohammad Reza Mosavi, Diego Martín de Andrés,
Volume 22, Issue 0 (3-2026)
Abstract
Global Positioning System (GPS) spoofing poses serious threats to navigation systems, as it transmits false GPS signals that cause receivers to compute incorrect positions. To address this issue, our research in this study focused on leveraging the Cross-Ambiguity Function (CAF) along with advanced machine learning techniques to effectively detect spoofing attacks. A further challenge in using CAF for spoofing detection is its high dimensionality, which demands powerful hardware and considerably slows down the detection process. Detecting spoofing signals with delays of less than 0.5 chips relative to the authentic signal is particularly difficult. To overcome this, the SVD_Var dimensionality reduction algorithm, which leverages the variance of CAF data through Singular Value Decomposition (SVD), is proposed to enhance both speed and detection performance. The reduced-dimensionality data are subsequently used to train a basic Multi-Layer Perceptron (MLP) neural network and the k-Nearest Neighbors (kNN) algorithm. The effectiveness of the proposed method is validated using the widely recognized Texas Spoofing Test Battery (TEXBAT) dataset. Results indicate that the method achieves an average detection rate exceeding 80% across various TEXBAT scenarios, demonstrating enhanced sensitivity and robustness in spoofing detection compared to both traditional and state-of-the-art approaches. Also, this approach accomplishes a dimensionality reduction ranging from 99.69% to 99.99% in terms of the number of pixels which significantly accelerates the processing speed.
S. Tohidi, M. R. Mosavi,
Volume 22, Issue 1 (3-2026)
Abstract
A vital part of people's daily life is the position, navigation, and time service provided by the Global Positioning System (GPS), which is always accessible globally. Consequently, the security of the GPS receivers is crucial. Occasionally, intentional and unintentional interferences cause GPS location issues. Spoofing attack is the most severe interference to the GPS receivers, which results in positional mistakes. This paper's goal is to defend against the carry-off spoofing attacks. In a carry-off spoofing attempt, the spoofer transmits signals whose code phase and carrier frequency parameters are strikingly close to the actual signal in order to change the correlation values generated in the tracking stage. Discriminator output values alter as correlation values change. As a result, the Pseudo Random Noise (PRN) code generator unit creates a local replica, which forces the tracking loop to follow the fake signal instead of the real one. It is proposed in this paper that when spoofing attacks occur, discriminator output values be generated independently of correlation values. Specifically, when a spoofing signal is detected, the conventional discriminator is replaced by a Non-linear Autoregressive Exogenous Neural Network (NARX NN)-based predictor. This strategy protects the tracking loop from the effects of the spoofing signal. The efficiency of the provided strategy was evaluated using three spoofing data sets. The results of the suggested mitigation method, based on NARAX NN, show that it mitigates spoofing attacks by an average of 95.82%.
Maryam Moazedi, Mohammad Reza Mosavi, Diego Martín de Andrés,
Volume 22, Issue 3 (9-2026)
Abstract
The Receiver Autonomous Integrity Monitoring (RAIM) method uses additional information to detect and remove spoofing signals by analyzing pseudo-range measurements. Therefore, assuming that spoofing signals are errors for the valid signal, RAIM can be a practical method that does not impose expensive hardware to the receiver. Typically, RAIM operates under the assumption that simultaneous multi-satellite errors are highly unlikely. For example, GPS satellite errors occur no more than three times per year. Some enhanced RAIM methods have been proposed in recent years that employ additional measurements, such as Doppler shift measurements, time-differential carrier phase measurements, and so on. Since simultaneous multiple fake satellites are common in spoofing cases, basic RAIM cannot counter these types of signals, and for eliminating more than one spoofing or error signal requires additional information, such as measurements on other frequencies or satellite systems, which increases the complexity of execution. In this paper, an anti-spoofing method based on Advanced RAIM (ARAIM) has been proposed with a novel slope-based RAIM availability assessment method. Simulation results on several spoofing data sets indicate the definitive success of the proposed methods in detecting and mitigating spoofing error, with a detection success rate of over 79% using the statistical method and over 87% using the Kalman filter method.
