DeepSIM: GPS Spoofing Detection on UAVs using Satellite Imagery Matching

Xue, Nian; Niu, Liang; Hong, Xianbin; Li, Zhen; Hoffaeller, Larissa; Pöpper, Christina

doi:10.1145/3427228.3427254

Cited by 36 publications

(14 citation statements)

References 36 publications

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“…In this case, the drawbacks are the same as the previous method; additionally, this method relies on two sensors: the camera and IMU. A deep learning-based solution which uses images from satellites and compares them to images from a drone's camera to determine whether the locations match was proposed by [11]; its disadvantage is that it requires initial preparation in the flight area. Moreover, every GPS point in the area must be covered, a requirement which increases the size of the precompiled database significantly.…”

Section: Related Workmentioning

confidence: 99%

“…The significance of our method with respect to the methods proposed in related work is as follows. Our method (1) relies on existing hardware: in contrast to methods presented in other studies (e.g., [1], [4], and [9]), our method does not involve the use of additional hardware which makes the method also costeffective; (2) is database independent: in contrast to methods presented in other studies (e.g., [11]) which use a precompiled database, our method does not rely on a precompiled database or a map of the drone's flight area; and (3) our method offers flexibility: unlike other methods, it can be implemented either on the drone or at the ground control station used to control the drone (i.e., on the drone's controller). In addition (4), we empirically evaluate the accuracy of our method and determine the level of security for a situation in which the spoofed location is an average of 2.5 meters away from the actual location, an aspect that was not evaluated in related studies.…”

Section: Introductionmentioning

confidence: 99%

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VISAS -- Detecting GPS spoofing attacks against drones by analyzing camera's video stream

Davidovich¹,

Nassi²,

Elovici³

2022

Preprint

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In this study, we propose an innovative method for the real-time detection of GPS spoofing attacks targeting drones, based on the video stream captured by a drone's camera. The proposed method collects frames from the video stream and their location (GPS); by calculating the correlation between each frame, our method can identify an attack on a drone. 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 an altitude of 50-100 meters over an urban area at an average speed of 4 km/h in conditions of low ambient light; in this scenario, the method can provide a level of security that detects any GPS spoofing attack in which the spoofed location is a distance of 1-4 meters (an average of 2.5 meters) from the real location.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

VISAS -- Detecting GPS spoofing attacks against drones by analyzing camera's video stream

Davidovich¹,

Nassi²,

Elovici³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Zeng et al [62] demonstrate the insecurity of road navigation systems via a stealthy manipulation based on GPS spoofing. Considering multiple sensors, countermeasures exist for the detection of GPS spoofing attacks [20], [58], [61] and also for spoofer localization [19], [61]. However, these countermeasures depend on ground-based sensors and do not exploit the network volatility.…”

Section: Related Workmentioning

confidence: 99%

Trust the Crowd: Wireless Witnessing to Detect Attacks on ADS-B-Based Air-Traffic Surveillance

Jansen

Niu

Xue

et al. 2021

Proceedings 2021 Network and Distributed System Security Symposium

Self Cite

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Automatic Dependent Surveillance-Broadcast (ADS-B) has been widely adopted as the de facto standard for air-traffic surveillance. Aviation regulations require all aircraft to actively broadcast status reports containing identity, position, and movement information. However, the lack of security measures exposes ADS-B to cyberattacks by technically capable adversaries with the purpose of interfering with air safety. In this paper, we develop a non-invasive trust evaluation system to detect attacks on ADS-B-based air-traffic surveillance using real-world flight data as collected by an infrastructure of ground-based sensors. Taking advantage of the redundancy of geographically distributed sensors in a crowdsourcing manner, we implement verification tests to pursue security by wireless witnessing. At the core of our proposal is the combination of verification checks and Machine Learning (ML)-aided classification of reception patterns-such that user-collected data cross-validates the data provided by other users. Our system is non-invasive in the sense that it neither requires modifications on the deployed hardware nor the software protocols and only utilizes already available data. We demonstrate that our system can successfully detect GPS spoofing, ADS-B spoofing, and even Sybil attacks for airspaces observed by at least three benign sensors. We are further able to distinguish the type of attack, identify affected sensors, and tune our system to dynamically adapt to changing air-traffic conditions.

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“…The significance of our method with respect to the methods proposed in related work is as follows: Our method relies on existing hardware: in contrast to methods presented in other studies (e.g., [ 2 , 3 , 4 ]), our method does not involve the use of additional hardware, which also makes it cost-effective; Our method is database independent: in contrast to methods presented in other studies (e.g., [ 5 ]) which use a precompiled database, our method does not rely on a precompiled database or a map of the drone’s flight area; Our method offers flexibility: unlike other methods, it can be implemented on the drone itself or from the ground control station used to control the drone (i.e., on the drone’s controller); We empirically evaluate the accuracy of our method and determine the level of security for a situation in which the spoofed location is an average of 2.5 m away from the actual location, an aspect that was not evaluated in related studies. …”

Section: Introductionmentioning

confidence: 98%

“…Our method is database independent: in contrast to methods presented in other studies (e.g., [ 5 ]) which use a precompiled database, our method does not rely on a precompiled database or a map of the drone’s flight area;…”

Section: Introductionmentioning

confidence: 99%

Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera’s Video Stream

Davidovich

Nassi

Elovici

2022

Sensors

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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.

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DeepSIM: GPS Spoofing Detection on UAVs using Satellite Imagery Matching

Cited by 36 publications

References 36 publications

VISAS -- Detecting GPS spoofing attacks against drones by analyzing camera's video stream

VISAS -- Detecting GPS spoofing attacks against drones by analyzing camera's video stream

Trust the Crowd: Wireless Witnessing to Detect Attacks on ADS-B-Based Air-Traffic Surveillance

Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera’s Video Stream

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