Crowd-GPS-Sec: Leveraging Crowdsourcing to Detect and Localize GPS Spoofing Attacks

Jansen, Kai; Schäfer, Matthias; Moser, Daniel; Lenders, Vincent; Pöpper, Christina; Schmitt, Jens

doi:10.1109/sp.2018.00012

Cited by 102 publications

(57 citation statements)

References 25 publications

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“…Different techniques have been proposed in literature to defend against GPS spoofing attacks with a focus on attack detection [16]- [20]. In [16], different techniques are discussed that can enable a UAV's receiver to detect the spoofing attacks.…”

Section: A Related Workmentioning

confidence: 99%

“…In [21], automatic gain control is used within the GPS receiver to detect and flag potential spoofing attacks within a low computational complexity framework. Finally, the work in [20] proposed a technique that allows UAVs to detect GPS spoofers by using an independent ground infrastructure that continuously analyzes the contents and times of arrival of the estimated UAV positions. The proposed technique was shown to be effective in detecting the spoofing attacks in less than two seconds and to determine the spoofer's location after 15 minutes of monitoring time, with an accuracy of up 150 meters.…”

Section: Arxiv:190411568v3 [Cssy] 29 Dec 2019mentioning

confidence: 99%

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

Eldosouky

Ferdowsi

Saad

2020

IEEE Internet Things J.

113

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One prominent security threat that targets unmanned aerial vehicles (UAVs) is the capture via GPS spoofing in which an attacker manipulates a UAV's global positioning system (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 paper, 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. 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.

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Section: A Related Workmentioning

confidence: 99%

Section: Arxiv:190411568v3 [Cssy] 29 Dec 2019mentioning

confidence: 99%

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

Eldosouky

Ferdowsi

Saad

2020

IEEE Internet Things J.

113

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“…Volunteers around the world set up and operate large numbers of receivers for transponder signals and send the live tracking data to a central server via the Internet. This approach has been used to improve the security in GPS [26].…”

Section: Secure Location Verification Technologymentioning

confidence: 99%

Multidevice False Data Injection Attack Models of ADS-B Multilateration Systems

Shang

Wang

Yan

et al. 2019

Security and Communication Networks

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Location verification is a promising approach among various ADS-B security mechanisms, which can monitor announced positions in ADS-B messages with estimated positions. Based on common assumption that the attacker is equipped with only a single device, this mechanism can estimate the position state through analysis of time measurements of messages using multilateration algorithm. In this paper, we propose the formal model of multidevice false data injection attacks in the ATC system against the location verification. Assuming that attackers equipped with multiple devices can manipulate the ADS-B messages in distributed receivers without any mutual interference, such attacker can efficiently construct attack vectors to change the results of multilateration. The feasibility of a multidevice false data injection attack is demonstrated experimentally. Compared with previous multidevice attacks, the multidevice false data injection attacks can offer lower cost and more covert attacks. The simulation results show that the proposed attack can reduce the attackers’ cost by half and achieve better time synchronization to bypass the existing anomaly detection. Finally, we discuss the real-world constraints that limit their effectiveness and the countermeasures of these attacks.

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“…the OpenSky Network [20], Flightaware [5], Flightradar24 [6] and many others. These sensor networks can leverage the time-of-arrival (TOA) of Mode S signals for various kinds of applications, including aircraft localization [20,22], air traffic data verification [13,16,17,19,21], and self-localization [15]. In those applications, a set of cooperating receivers measure locally the TOA of the arriving signals and then send these data to a central computation server.…”

Section: Introductionmentioning

confidence: 99%

Nanosecond-Precision Time-of-Arrival Estimation for Aircraft Signals with Low-Cost SDR Receivers

Calvo-Palomino

Ricciato

Repas

et al. 2018

2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

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Precise Time-of-Arrival (TOA) estimations of aircraft and drone signals are important for a wide set of applications including aircraft/drone tracking, air traffic data verification, or self-localization. Our focus in this work is on TOA estimation methods that can run on low-cost software-defined radio (SDR) receivers, as widely deployed in Mode S / ADS-B crowdsourced sensor networks such as the OpenSky Network. We evaluate experimentally classical TOA estimation methods which are based on a cross-correlation with a reconstructed message template and find that these methods are not optimal for such signals. We propose two alternative methods that provide superior results for real-world Mode S / ADS-B signals captured with low-cost SDR receivers. The best method achieves a standard deviation error of 1.5 ns.

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Crowd-GPS-Sec: Leveraging Crowdsourcing to Detect and Localize GPS Spoofing Attacks

Cited by 102 publications

References 25 publications

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

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

Multidevice False Data Injection Attack Models of ADS-B Multilateration Systems

Nanosecond-Precision Time-of-Arrival Estimation for Aircraft Signals with Low-Cost SDR Receivers

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