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The growing use of wireless technologies in power systems has raised concerns about cybersecurity, particularly regarding GPS spoofing attacks (GSAs). These attacks manipulate GPS data, leading to modifications in the phase angle of phasor measurement units (PMUs). In this paper, a Deep‐learning GPS‐Spoofing Counteraction (DLGSC) algorithm is proposed, utilizing PMU data for GSA detection and PMU data correction. The algorithm incorporates a recurrent neural network (RNN) and a set of long short‐term memory (LSTM) units separately, for signal correction after attack detection. Unlike existing methods that struggle with simultaneous attacks or they are static methods, DLGSC tackles these challenges by leveraging deep learning techniques. By selecting appropriate features for GSA detection, DLGSC achieves accurate results. The algorithm is evaluated on standard IEEE 14‐bus and IEEE 39‐bus power systems, and its performance is compared to statistical, dynamic, and Deep Learning (DL) methods in the literature. Additionally, an experimental setup is designed to validate the algorithm in a laboratory environment. Results demonstrate the easy‐implementable DLGSC algorithm's satisfactory real‐time performance in various scenarios, such as load variations and noise, achieving over 98% accuracy. Notably, DLGSC is cable of detecting multiple GSAs on different PMUs. [ABSTRACT FROM AUTHOR]

A Global Positioning System (GPS) spoofing attack can be launched against any commercial GPS sensor in order to interfere with its navigation capabilities. These sensors are installed in a variety of devices and vehicles (e.g., cars, planes, cell phones, ships, UAVs, and more). In this study, we focus on micro UAVs (drones) for several reasons: (1) they are small and inexpensive, (2) they rely on a built-in camera, (3) they use GPS sensors, and (4) it is difficult to add external components to micro UAVs. We propose an innovative method, based on the video stream captured by a drone's camera, for the real-time detection of GPS spoofing attacks targeting drones. The proposed method collects frames from the video stream and their location (GPS coordinates); by calculating the correlation between each frame, our method can detect GPS spoofing attacks on drones. We first analyze the performance of the suggested method in a controlled environment by conducting experiments on a flight simulator that we developed. Then, we analyze its performance in the real world using a DJI drone. Our method can provide different levels of security against GPS spoofing attacks, depending on the detection interval required; for example, it can provide a high level of security to a drone flying at altitudes of 50–100 m over an urban area at an average speed of 4 km/h in conditions of low ambient light; in this scenario, the proposed method can provide a level of security that detects any GPS spoofing attack in which the spoofed location is a distance of 1–4 m (an average of 2.5 m) from the real location. [ABSTRACT FROM AUTHOR]

Most of Flying Adhoc Networks (FANETs) applications consume GPS based location information for their services, which are also shared in real-time with other UAVs, ground control stations and centralized service operators. GPS spoofing is among the most popular attacks in FANETs that lead the Global Navigation Satellite System (GNSS) receivers to generate false navigation solutions. Several anti-spoofing techniques have been proposed in the literature. However, Conventional detection methods present vulnerabilities against Colluding GPS Spoofing Attack where multiple GPS spoofing signal sources are used. In this paper, we propose a policy-based distributed detection mechanism to face colluding GPS-Spoofing attack in FANETs. Based on Burglary scene and the location of each UAV at the time of the attack, we can distinguish between Active and Passive witnesses that are respectively used to help the target to detect and confirm the presence of GPS spoofing signal using respectively Absolute power and Carrier-to-Noise density ratio. A trust model based on Beta and Weibull Distribution has been adopted as a combination technique of different testimonies to both mitigate the spread of false rumors and classify the different signals. Simulation results depict that our proposal is able to detect and revoke the GPS Spoofing signal with high accuracy reaching the 99% and low communication overhead. [ABSTRACT FROM AUTHOR]

GPS spoofing is a problem that is receiving increasing scrutiny due to an increasing number of reported attacks. Plenty of results have been reported on detecting the presence of GPS spoofing attacks. However, very few results currently exist for the localization of spoofing attackers, which is crucial to counteract GPS attacks. In this paper we propose leveraging vehicle-to-vehicle communications to detect and localize spoofing attacks on vehicular navigation GPS. The key idea is to correlate Doppler shift measurements which are reported by most commercial GPS receivers. The approach does not need additional dedicated devices and is easily deployable on modern vehicles equipped with vehicle-to-vehicle communication devices. It is capable of localizing both stationary spoofers and mobile spoofers which, for example, could be mounted on a vehicle. Both numerical simulations and experimental tests are conducted to confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This document presents the development of a cybersecurity platform for unmanned aerial vehicles (UAV), or drones, based on cyber-deception technology. This technology is based on the deliberate creation of easily accessible and detectable decoys (or honeypots) to detect potential intruders at the perimeter of a critical infrastructure. This document contains an explanation of the conceptual model of the proposed solution, and a detailed description of each of the developed procedures. A technological development methodology was implemented in this paper. Also, procedures were developed for the creation of honeypots in Wi-Fi wireless networks and in radio frequency bands, and the approach to the development of a GPS spoofing procedure was included. The latter was explored using software defined radio equipment (SDR) for the impersonation of GPS signals, which allows for the implementation of defense strategies against attacking drones. Finally, a web platform to monitor active decoys, and to register penetration attempts, is described. These penetration attempt records are stored in a blockchain, developed with basis on Ethereum technology. It was found that Wi-Fi and radio frequency wireless networks have vulnerabilities that can be exploited by potential attackers. Also, that the GPS spoofing procedure is much more complex than procedures by wireless networks, but it would allow to take action on attacking drones. [ABSTRACT FROM AUTHOR]

Cyberinfrastructure (e.g., sensors, actuators and the associated communication network) has become an integral part of our modern power grid. While these cyber technologies enhance situational awareness and operational efficiency, they also expose the physical system to cyber-attacks. In this paper, we consider the problem of transmission system state estimation based on measurements from a number of PMUs. In this context, two PMU data integrity attacks namely, Time Synchronization Attack (TSA) and Man-in-the-Middle (MitM) attacks that can potentially cause a severe impact on the grid, are analyzed. Specifically, we propose a novel method based on an alternate expectation–maximization framework to mitigate the effects of these attacks on the state estimation process. Numerical tests are conducted on IEEE-14, 30 and 118 bus systems with different attack scenarios to validate the developed method. Unlike existing works, the proposed algorithm provides accurate state estimates without any prior knowledge of the location of the attack, the number of meters being attacked, or the magnitude of the attack parameter. • Robust state estimation approach to mitigate the impact of cyber-attack on PMU • "Unordered sensing" based perspective to formulate the estimation problem • Presents a quantitative analysis of computational complexity and simulation time. • Estimation error bound in the presence of cyber-attack. [ABSTRACT FROM AUTHOR]

The number of incidents between unmanned aerial vehicles (UAVs) and aircrafts at airports and airfields has been increasing over the last years. To address the problem, in this paper we describe a portable system capable of protecting areas against unauthorized UAVs, which is based on the use of low-cost SDR (software defined radio) platforms. The proposed anti-UAV system supports target localization and integrates effective jamming techniques with the generation of global positioning system (GPS) spoofing signals aimed at the drone. Real-life tests of the implemented prototype have shown that the proposed approach is capable of stopping the reliable reception of radionavigation signals and can also divert or even take control of unauthorized UAVs, whose flight path depends on the information obtained by the GPS system. [ABSTRACT FROM AUTHOR]

With the fast growth of civilian drones, their security problems meet significant challenges. A commercial drone may be hijacked by Global Positioning System (GPS)-spoofing attacks for illegal activities, such as terrorist attacks. Ideally, comparing positions respectively estimated by GPS and Inertial Navigation System (INS) can detect such attacks, while the results may always get fault because of the accumulated errors over time in INS. Therefore, in this paper, we propose a two-step GA-XGBoost method to detect GPS-spoofing attacks that just uses GPS and Inertial Measurement Unit (IMU) data. However, tunning the proper values of XGBoost parameters directly on the drone to achieve high prediction results consumes lots of resources which would influence the real-time performance of the drone. The proposed method separates the training phase into offboard step and onboard step. In offboard step, model is first trained by flight logs, and the training parameter values are automatically tuned by Genetic Algorithm (GA). Once the offboard model is trained, it could be uploaded to drones. To adapt our method to drones with different types of sensors and improve the correctness of prediction results, in onboard step, the model is further trained when a drone starts a mission. After onboard training finishes, the proposed method switches to the prediction mode. Besides, our method does not require any extra onboard hardware. The experiments with a real quadrotor drone also show the detection correctness is 96.3% and 100% in hijacked and non-hijacked cases at each sampling time respectively. Moreover, our method can achieve 100% detection correctness just within 1 s just after the attacks start. [ABSTRACT FROM AUTHOR]