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.

Multicarrier wireless communication systems, which usually operate over frequency-selective fading channels, are practically also impaired by environmental impulsive noise. In order to boost the signal-to-noise power ratio (SNR) at the receiver, this paper proposes and analyzes nonlinear estimators based on the multiple thresholding, with associated piecewise attenuation, or clipping, of the received signal amplitude. The proposed approach exploits a new heuristic criterion to set the thresholds. Although the obtained thresholds are slightly suboptimal in output SNR, the proposed approach has the nice and useful property to allow closed-form analytical derivations for both the threshold(s) and the associated attenuating/clipping parameters, when a Gaussian source is impaired by an impulsive noise. Such a quite general class of estimators, which could be applied also in other scenarios, is particularly attractive for orthogonal frequency division multiplexing communication systems in the presence of additive multicomponent impulsive noise. We are also letting to derive closed-form expressions for the output SNR in frequency-selective multipath fading channels. The SNR performance and the associated symbol error rate have been compared with those of more traditional blanking, clipping, and clipping-blanking processors. Specifically, analytical and simulation results, carried out for Class-A and symmetrical alpha-stable (SαS) impulsive noise, show that the proposed threshold-based suppressors are superior to the traditional ones. Furthermore, as the number of thresholds increases, the proposed estimators closely approach the performance of the optimal minimum mean square error Bayesian estimator. However, as it is shown, in practical conditions only few thresholds are necessary.

Wireless microsensor networks have been identified as one of the most important technologies for the 21st century. Cheap, smart devices with multiple onboard sensors, networked through wireless links and the Internet and deployed in large numbers, provide unprecedented opportunities for instrumenting and controlling homes, cities, and the environment. One of the crucial issues in wireless sensor networks is position determination. In this work a positioning system based on received signal strength (RSS) and WLAN is presented. In indoor environments, received signal strength is a complex function of distance. In this work artificial neural network is used to establish a relationship between RSS and location. The location determination accuracy of the proposed system has been investigated and promising results have been achieved. Although based on WLAN technology, the same positioning technique can be applied to any wireless mobile device or sensor in a wireless sensor networks.

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.

LoRaWANs are increasingly recognized for their vulnerability to various jamming attacks, which can significantly disrupt communication between end nodes and gateways. This paper explores the feasibility of executing reactive jammers upon detecting packet transmission using commercially available equipment based on software-defined radios (SDRs). The proposed approach demonstrates how attackers can exploit packet detection to initiate targeted interference, effectively compromising message integrity. Two distinct experimental setups, one using separate SDRs for reception and transmission, and another leveraging a single SDR for both functions, were used to evaluate attack efficiency, reaction times, and packet loss ratios. Our experiments demonstrate that both scenarios effectively jam LoRaWAN packets across a range of spreading factors and payload sizes. This finding underscores a pressing need for enhanced security measures to maintain reliability and counter sophisticated attacks.