GPS Spoofing Attacks Research Articles

An Approach of Linear Regression‐Based UAV GPS Spoofing Detection

A prominent security threat to unmanned aerial vehicle (UAV) is to capture it by GPS spoofing, in which the attacker manipulates the GPS signal of the UAV to capture it. This paper introduces an anti‐spoofing model to mitigate the impact of GPS spoofing attack on UAV mission security. In this model, linear regression (LR) is used to predict and model the optimal route of UAV to its destination. On this basis, a countermeasure mechanism is proposed to reduce the impact of GPS spoofing attack. Confrontation is based on the progressive detection mechanism of the model. In order to better ensure the flight security of UAV, the model provides more than one detection scheme for spoofing signal to improve the sensitivity of UAV to deception signal detection. For better proving the proposed LR anti‐spoofing model, a dynamic Stackelberg game is formulated to simulate the interaction between GPS spoofer and UAV. In particular, for GPS spoofer, it is worth mentioning that for the scenario that the UAV is cheated by GPS spoofing signal in the mission environment of the designated route is simulated in the experiment. In particular, UAV with the LR anti‐spoofing model, as the leader in this game, dynamically adjusts its response strategy according to the deception’s attack strategy when upon detection of GPS spoofer’s attack. The simulation results show that the method can effectively enhance the ability of UAV to resist GPS spoofing without increasing the hardware cost of the UAV and is easy to implement. Furthermore, we also try to use long short‐term memory (LSTM) network in the trajectory prediction module of the model. The experimental results show that the LR anti‐spoofing model proposed is far better than that of LSTM in terms of prediction accuracy.

Detection of global positioning system spoofing attack on unmanned aerial vehicle system

SummaryMost of the existing global positioning system (GPS) spoofing detection schemes are vulnerable to the generative GPS spoofing attack, or require additional auxiliary equipment and extensive signal processing capabilities, leading to defects such as low real‐time performance and large communication overhead which are not available for the unmanned aerial vehicle (UAV, also known as drone) system. Therefore, we propose a novel solution which employs information fusion based on the GPS receiver and inertial measurement unit. We use a real‐time model of tracking and calculating to derive the current position of the drones which are then contrasted with the position information received by the receiver to verify whether the presence or absence of spoofing attack. Subsequent experimental work shows that, the proposed method can accurately detect the spoof within 8 seconds, with a detection rate (DR) of 98.6%. Compared with the existing schemes, the performance of real‐time detecting is improved while the DR is ensured. Even in our worst‐case, we detect the spoof within 28 seconds after the UAV system starts its mission.

Dynamic GPS Spoofing Attack Detection, Localization, and Measurement Correction Exploiting PMU and SCADA

GPS receiver is the source of synchronization in phasor measurement unit (PMU) networks. While it guarantees time precision, it is vulnerable to cyberattacks, which may modify the time offset and results in mismatches in time. This modification (GPS spoofing attack) changes the phase angle of signals measured by PMU. This article deals with three important challenges in GPS spoofing attack detection; first, multiattack detection while there are probable attacks on different PMUs, second, attack detection considering the dynamic model of power systems, and third, measurement correction. For providing solution to the abovementioned problems, an anti-GPS spoofing attack mechanism is suggested, in which, a dynamic filter with the help of PMU and supervisory control and data acquisition (SCADA) systems measurements estimate the phase shifts caused by spoofing attacks. The results of data fusion of PMUs and SCADA in the detection method are shown. Numerical results demonstrate that this approach can detect dynamic spoofing attacks in a reasonable time and as soon as multiple attacks are detected, corrected measurements are provided.

Localizing Spoofing Attacks on Vehicular GPS Using Vehicle-to-Vehicle Communications

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.

Sensitive Detection of GPS Spoofing Attack in Phasor Measurement Units via Quasi-Dynamic State Estimation

We propose the use of a quasi-dynamic-state estimator to detect GPS spoofing attacks and assist in recovering from attacks on power grids. Numerical results show that this method is sensitive to GPS spoofing attacks with robust performance regardless of measurement noise.

Prediction‐discrepancy based on innovative particle filter for estimating UAV true position in the presence of the GPS spoofing attacks

In this paper, a novel prediction-discrepancy based on innovative particle filter (PDIPF) is proposed to solve the unmanned aerial vehicle (UAV) positioning problem in the presence of the global positioning system (GPS) spoofing attack, supposing that the GPS spoofing effects are in the form of unknown but bounded errors. To cope with the GPS spoofing attacks as unknown sudden changes of system state variables, the compensation of the GPS spoofing effects is adaptively done in two basic parts of PDIPF algorithm including particle weighting and covariance matrix adaption. In addition, a theorem is developed which verifies that the output estimation error is upper bounded by a given probability with the help of the adapted covariance matrix. Besides, the particle weight calculation in PDIPF is done with respect to the prediction discrepancy of generated particles from the GPS measurements. The proposed PDIPF is used to decrease the effects of any GPS spoofing errors with different probability density functions and estimate true position of UAV in the presence of the GPS spoofing attacks. The algorithm is applied to the inertial navigation system/GPS/Loran-C integration systems. Simulation results demonstrate the effectiveness of the proposed PDIPF algorithm in terms of accuracy and redundancy.

Drones in Distress: A Game-Theoretic Countermeasure for Protecting UAVs Against GPS Spoofing

One prominent security threat that targets unmanned aerial vehicles (UAVs) is the capture via global positioning system (GPS) spoofing in which an attacker manipulates a UAV’s GPS signals in order to capture it. Given the anticipated widespread deployment of UAVs for various purposes, it is imperative to develop new security solutions against such attacks. In this article, a mathematical framework is introduced for analyzing and mitigating the effects of GPS spoofing attacks on UAVs. In particular, system dynamics are used to model the optimal routes that the UAVs will adopt to reach their destinations. The GPS spoofer’s effect on each UAV’s route is also captured by the model. To this end, the spoofer’s optimal imposed locations on the UAVs, are analytically derived; allowing the UAVs to predict their traveling routes under attack. Then, a countermeasure mechanism is developed to mitigate the effect of the GPS spoofing attack. The countermeasure is built on the premise of cooperative localization, in which a UAV can determine its location using nearby UAVs instead of the possibly compromised GPS locations. To better utilize the proposed defense mechanism, a dynamic Stackelberg game is formulated to model the interactions between a GPS spoofer and a drone operator. In particular, the drone operator acts as the leader that determines its optimal strategy in light of the spoofer’s expected response strategy. The equilibrium strategies of the game are then analytically characterized and studied through a novel proposed algorithm. The simulation results show that, when combined with the Stackelberg strategies, the proposed defense mechanism will outperform baseline strategy selection techniques in terms of reducing the possibility of UAV capture.

Attack Identification and Correction for PMU GPS Spoofing in Unbalanced Distribution Systems

Due to the vulnerability of civilian global positioning system (GPS) signals, the accuracy of phasor measurement units (PMUs) can be greatly compromised by GPS spoofing attacks (GSAs), which introduce phase shifts into true phase angle measurements. Focusing on simultaneous GSAs for multiple PMU locations, this paper proposes a novel identification and correction algorithm in distribution systems. A sensitivity analysis of state estimation residuals on a single GSA phase angle is firstly implemented. An identification algorithm using a probing technique is proposed to determine the locations of spoofed PMUs and the ranges of GSA phase shifts. Based on the identification results, these GSA phase shifts are determined via an estimation algorithm that minimizes the mismatch between measurements and system states. Further, with the attacked PMU data corrected, the system states are recovered. Simulations in unbalanced IEEE 34-bus and 123-bus distribution systems demonstrates the efficiency and accuracy of the proposed method.

Multi-period Power System State Estimation with PMUs Under GPS Spoofing Attacks

This paper introduces a dynamic network model together with a phasor measurement unit (PMU) measurement model suitable for power system state estimation under spoofing attacks on the global positioning system (GPS) receivers of PMUs. The spoofing attacks may introduce time-varying phase offsets in the affected PMU measurements. An algorithm is developed to jointly estimate the state of the network, which amounts to the nodal voltages in rectangular coordinates, as well as the time-varying attacks. The algorithm features closed-form updates. The effectiveness of the algorithm is verified on the standard IEEE transmission networks. It is numerically shown that the estimation performance is improved when the dynamic network model is accounted for compared with a previously reported static approach.

Efficient drone hijacking detection using two-step GA-XGBoost

Abstract 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.

Operator Strategy Model Development in UAV Hacking Detection

An increasingly relevant security issue for unmanned aerial vehicles (UAVs, also known as drones) is the possibility of a global positioning system (GPS) spoofing attack. Given the existing problems in current GPS spoofing detection techniques and human visual advantages in searching and localizing targets, we propose a human-autonomy collaborative approach of human geo-location to assist UAV control systems in detecting GPS spoofing attacks. An interactive testbed and experiment were designed and used to evaluate this approach, which demonstrated that human-autonomy collaborative hacking detection is a viable concept. Using the hidden Markov model (HMM) approach, operator behavior patterns and strategies from the experiment were modeled via hidden states and transitions among them. These models revealed two dominant hacking detection strategies. Statistical results and expert performer evaluations show no significant difference between different hacking detection strategies in terms of correct detection. The detection strategy model suggests areas of future research in decision support tool design for UAV hacking detection. Also, the development of HMMs presents the feasibility of quantitatively investigating operator behavior patterns and strategies in human supervisory control scenarios.

GPS spoofing detection for the power grid network using a multireceiver hierarchical framework architecture

In the future, the modernized power grid, or smart grid, will utilize devices called phasor measurement units (PMUs) to continuously monitor the power grid state in real time. These devices utilize GPS to synchronize the voltage and current phasor measurements across the continental network; however, because the civilian GPS signals are unencrypted, PMUs are susceptible to GPS spoofing attacks. We propose a spoofing detection algorithm using a wide-area, hierarchical architecture. In the network, each PMU transmits conditioned signal fragments containing the military P(Y) signal, which serves as an encrypted signature in the background of all authentic GPS signals. This signature is then verified among a subnetwork consisting of a select number of well-dispersed receivers. We subsequently compare representative signals generated for each subnetwork in order to detect coordinated attacks against the subnetwork receiver collection. Using real-world data recorded during a government-sponsored, live-sky spoofing event, we demonstrate that our algorithm successfully evaluates the authenticity of a widely dispersed receiver network.

Covert Spoofing Algorithm of UAV Based on GPS/INS-Integrated Navigation

A covert spoofing algorithm of an unmanned aerial vehicle (UAV) based on GPS/inertial-navigation-system-integrated navigation is analyzed in this paper. The proposed algorithm theoretically proves the fact that when the acceleration component of the counterfeit GPS signal is the difference between the UAV's current acceleration and the spoofing control input, the UAV can be covert spoofed. To avoid adverse consequences (detection or crash) caused by frequent changes in UAV flight during the GPS spoofing attacks, the proposed algorithm requires that the deception trajectory planned by the GPS spoofer is slowly changing relative to the reference trajectory, which is pre-set by the UAV. Simulation results have verified the correctness of the proposed covert spoofing algorithm of UAV. Moreover, the spoofing effect is more distinct when the deception trajectory planning is considered.

Vulnerability Analysis of Smart Grids to GPS Spoofing

Sensors such as phasor measurement units (PMUs) endowed with GPS receivers are ubiquitously installed providing real-time grid visibility. A number of PMUs can cooperatively enable state estimation routines. However, GPS spoofing attacks can notably alter the PMU measurements, mislead the network operator, and drastically impact subsequent corrective control actions. Leveraging a novel measurement model that explicitly accounts for the GPS spoofing attacks, this paper formulates an optimization problem to identify the most vulnerable PMUs in the network. A greedy algorithm is developed to solve the aforementioned problem. Furthermore, the paper develops a computationally efficient alternating minimization algorithm for joint state estimation and attack reconstruction. Numerical tests on IEEE benchmark networks validate the developed methods.

Drone forensics: examination and analysis

Unmanned aerial vehicles (UAVs), also known as drones, provides unique functionalities, which allows area surveillance, inspection, surveying, unarmed cargo, armed attack machines, and aerial photography. Drones are susceptible to GPS spoofing attacks, integrity attacks and de-authentication attacks, which can allow criminals to access data, intercept the drone and use it commit a crime. Thus, this paper is presented to report on potential attacks against the Parrot Bebop 2 drone, and the ability for an investigator to collect evidence about the attacks on the drone. This paper aims at examining the possibility of establishing ownership and collecting data to reconstruct events, linking the drone controller with the drone to prove ownership, flight origins and other potentially useful information necessary to identify the proprietor of a crime. In addition, we have also proposed small-scale drone ontology for modelling drone context data, and simple forensic processing framework for small-scale drones.

An Efficient UAV Hijacking Detection Method Using Onboard Inertial Measurement Unit

With the fast growth of civil drones, their security problems meet significant challenges. A commercial drone may be hijacked by a GPS-spoofing attack for illegal activities, such as terrorist attacks. The target of this article is to develop a technique that only uses onboard gyroscopes to determine whether a drone has been hijacked. Ideally, GPS data and the angular velocities measured by gyroscopes can be used to estimate the acceleration of a drone, which can be further compared with the measurement of the accelerometer to detect whether a drone has been hijacked. However, the detection results may not always be accurate due to some calculation and measurement errors, especially when no hijacking occurs in curve trajectory situations. To overcome this, in this article, we propose a novel and simple method to detect hijacking only based on gyroscopes’ measurements and GPS data, without using any accelerometer in the detection procedure. The computational complexity of our method is very low, which is suitable to be implemented in the drones with micro-controllers. On the other hand, the proposed method does not rely on any accelerometer to detect attacks, which means it receives less information in the detection procedure and may reduce the results accuracy in some special situations. While the previous method can compensate for this flaw, the high detection results also can be guaranteed by using the above two methods. Experiments with a quad-rotor drone are conducted to show the effectiveness of the proposed method and the combination method.

Synchrophasor Data Correction Under GPS Spoofing Attack: A State Estimation-Based Approach

Global positioning system (GPS) spoofing attack (GSA) has been shown to be one of the most imminent threats to almost all cyber-physical systems incorporated with the civilian GPS signal. Specifically, for our current agenda of the modernization of the power grid, this may greatly jeopardize the benefits provided by the pervasively installed phasor measurement units (PMUs). In this paper, we consider the case where synchrophasor data from PMUs are compromised due to the presence of a single GSA, and show that it can be corrected by signal processing techniques. In particular, we introduce a statistical model for synchrophasor-based power system state estimation (SE), and then derive the spoofing-matched algorithms for synchrophasor data correction against GSA. Different testing scenarios in IEEE 14-, 30-, 57-, 118-bus systems are simulated to show the proposed algorithms’ performance on GSA detection and SE. Numerical results demonstrate that our proposed algorithms can consistently locate and correct the spoofed synchrophasor data with good accuracy as long as the system observability is satisfied. The accuracy of SE is significantly improved compared with the traditional weighted least square method and approaches the performance under the Genie-aided method.

A New Approach to Estimate True Position of Unmanned Aerial Vehicles in an INS/GPS Integration System in GPS Spoofing Attack Conditions

This paper presents a new approach to estimate the true position of an unmanned aerial vehicle (UAV) in the conditions of spoofing attacks on global positioning system (GPS) receivers. This approach consists of two phases, the spoofing detection phase which is accomplished by hypothesis test and the trajectory estimation phase which is carried out by applying the adapted particle filters to the integrated inertial navigation system (INS) and GPS. Due to nonlinearity and unfavorable impacts of spoofing signals on GPS receivers, deviation in position calculation is modeled as a cumulative uniform error. This paper also presents a procedure of applying adapted particle swarm optimization filter (PSOF) to the INS/GPS integration system as an estimator to compensate spoofing attacks. Due to memory based nature of PSOF and benefits of each particle’s experiences, application of PSOF algorithm in the INS/GPS integration system leads to more precise positioning compared with general particle filter (PF) and adaptive unscented particle filer (AUPF) in the GPS spoofing attack scenarios. Simulation results show that the adapted PSOF algorithm is more reliable and accurate in estimating the true position of UAV in the condition of spoofing attacks. The validation of the proposed method is done by root mean square error (RMSE) test.

Distributed Estimation of Power System Oscillation Modes Under Attacks on GPS Clocks

Phasor measurement units (PMUs) are playing an increasingly important role in wide-area monitoring and the control of power systems. PMUs allow synchronous real-time measurements of voltage, phase angle, and frequency from multiple remote locations in the grid, enabled by their ability to align to global positioning system (GPS) clocks. Given that this ability is vulnerable to GPS spoofing attacks, which have been confirmed easy to launch, in this paper, we propose a distributed real-time wide-area oscillation estimation approach that is robust to GPS spoofing on PMUs and their associated phasor data concentrators. The approach employs the idea of checking update consistency with histories and across distributed nodes and can tolerate up to one third of compromised nodes. It can be implemented in a completely decentralized architecture and in a completely asynchronous way. The effectiveness of the approach is confirmed by numerical simulations of the IEEE 68-bus power system models.

Imminent Communication Security for Smart Communities

A smart community is a collection of interdependent human-cyber-physical systems, in which the states of these systems are estimated and adapted by IoT technology. It enables sustainable societies that can offer increased well being, safety, and security. However, a smart community may be susceptible to novel forms of cyber-attacks. This article highlights GPS security vulnerabilities of unmanned ground vehicles (UGVs), which will become more popular in smart communities. We show that UGVs do not have adequate protection against GPS spoofing attacks. Consequently, they can easily be penetrated by attackers.