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.

Global Navigation Satellite Systems (GNSSs) are widely used for positioning, timing, and navigation services. Such widespread usage makes them exposed to various threats including malicious attacks such as spoofing attacks. The availability of low-cost devices such as software-defined radios enhances the viability of performing such attacks. Efficient spoofing detection is of essential importance for the mitigation of such attacks. Although various methods have been proposed for that purpose it is still an important research topic. In this paper, we investigate the spoofing detection method based on the integrated usage of discrete wavelet transform (DWT) and machine learning (ML) techniques and propose efficient solutions. A series of experiments using different wavelets and machine learning techniques for Global Positioning System (GPS) and Galileo systems are performed. Moreover, the impact of the usage of different types of training data are explored. Following the computational complexity analysis, the potential for complexity reduction is investigated and computationally efficient solutions proposed. The obtained results show the efficacy of the proposed approach.

With the development of new operating systems’ versions, smartphones are becoming more and more resistant to simplistic Global Navigation Satellite System (GNSS) spoofing attacks. However, the assessment of the resistance of the devices to the spoofing attacks is actual research topic. In this paper, the impact of a simplistic spoofing attack on a GPS unit, which provides location and navigation services, integrated into smartphones is investigated. Furthermore, simple and portable GPS spoofer is designed by using low-cost software-defined radio HackRF One and GPS-SDRSIM simulator. This paper contributes in range analysis of spoofer depending on different power levels and distances to smartphones. All experiments are successfully conducted on different smartphones whose location, date and time are spoofed in indoor environment. Results show the assessment of the resistance on simplistic spoofing attacks for different mobile operation systems considering different distances and transmitter power levels.

Nowadays, spoofing attacks are increasing due to the widespread usage of navigation and positioning systems. Another reason for increasing these attacks is the appearance and easy accessibility of low-cost software-defined radios (SDR). Such attacks need to be properly detected and, if possible, mitigated in time. Therefore, it is necessary to develop efficient systems for spoofing detection. There are many different spoofing detection methods which include hardware or software even machine learning methods. In this paper, detection and classification of spoofed Global navigation satellite system (GNSS) signals is performed by using different supervised machine learning methods and specific features. Two different datasets are used, including publicly available SatGrid dataset, which is not used in the examined and available articles as far to authors’ knowledge, and our adapted and modified dataset. Verification of our dataset, tested in the controlled indoor environment, is performed. Obtained results show that all used ML models classify signals with high accuracy. Two methods Support Vector Machine (SVM) and Neural Networks (NN) exhibit slightly better accuracy compared to other methods.

Presents information and current topics of interest to the global communications industry.