UAVs Path Deviation Attacks: Survey and Research Challenges

Sorbelli, Francesco Betti; Conti, Mauro; Pinotti, Cristina M.; Rigoni, Giulio

doi:10.1109/seconworkshops50264.2020.9149780

Cited by 13 publications

(3 citation statements)

References 29 publications

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“…However, they are significantly different from this paper. (1) Scope: some papers focus on the general security and safety problems in specific RV systems, e.g., drones [103]- [111], autonomous vehicles [45], [112]- [121]. Some papers just target one type of sensor spoofing, e.g., GPS [7]- [11], camera [12]- [15] or microphone [16], [88]).…”

Section: Systematization Scopementioning

confidence: 99%

SoK: Rethinking Sensor Spoofing Attacks against Robotic Vehicles from a Systematic View

Xu¹,

Han²,

Deng³

et al. 2022

Preprint

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Robotic Vehicles (RVs) have gained great popularity over the past few years. Meanwhile, they are also demonstrated to be vulnerable to sensor spoofing attacks. Although a wealth of research works have presented various attacks, some key questions remain unanswered: are these existing works complete enough to cover all the sensor spoofing threats? If not, how many attacks are not explored, and how difficult is it to realize them?This paper answers the above questions by comprehensively systematizing the knowledge of sensor spoofing attacks against RVs. Our contributions are threefold. (1) We identify seven common attack paths in an RV system pipeline. We categorize and assess existing spoofing attacks from the perspectives of spoofer property, operation, victim characteristic and attack goal. Based on this systematization, we identify 4 interesting insights about spoofing attack designs. (2) We propose a novel action flow model to systematically describe robotic function executions and sensor spoofing vulnerabilities. With this model, we successfully discover 104 spoofing attack vectors, 25 of which have been verified by prior works, while 55 attacks are practical but never considered. (3) We design two novel attack methodologies to verify the feasibility of newly discovered spoofing attack vectors.

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Section: Systematization Scopementioning

confidence: 99%

SoK: Rethinking Sensor Spoofing Attacks against Robotic Vehicles from a Systematic View

Xu¹,

Han²,

Deng³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, the authors of [21] review the use of Game Theory-based approaches for UAVs. UAV path deviation attacks have been surveyed in [22]. The authors of [14] provide a review of the characteristics and requirements of UAV networks for upcoming drone applications.…”

Section: A Related Surveys and Our Contributionsmentioning

confidence: 99%

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

Hassija

Chamola

Agrawal

et al. 2021

IEEE Commun. Surv. Tutorials

150

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Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resourceconstrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

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“…The existing methods of detecting and preventing spoofing attacks on navigation systems for software and hardware solutions were considered, and their advantages and disadvantages were highlighted [4,5,43,44].…”

mentioning

confidence: 99%

GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence

Basan

Nekrasov

et al. 2021

Drones

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Here, we developed a method for detecting cyber security attacks aimed at spoofing the Global Positioning System (GPS) signal of an Unmanned Aerial Vehicle (UAV). Most methods for detecting UAV anomalies indicative of an attack use machine learning or other such methods that compare normal behavior with abnormal behavior. Such approaches require large amounts of data and significant “training” time to prepare and implement the system. Instead, we consider a new approach based on other mathematical methods for detecting UAV anomalies without the need to first collect a large amount of data and describe normal behavior patterns. Doing so can simplify the process of creating an anomaly detection system, which can further facilitate easier implementation of intrusion detection systems in UAVs. This article presents issues related to ensuring the information security of UAVs. Development of the GPS spoofing detection method for UAVs is then described, based on a preliminary study that made it possible to form a mathematical apparatus for solving the problem. We then explain the necessary analysis of parameters and methods of data normalization, and the analysis of the Kullback—Leibler divergence measure needed to detect anomalies in UAV systems.

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UAVs Path Deviation Attacks: Survey and Research Challenges

Cited by 13 publications

References 29 publications

SoK: Rethinking Sensor Spoofing Attacks against Robotic Vehicles from a Systematic View

SoK: Rethinking Sensor Spoofing Attacks against Robotic Vehicles from a Systematic View

Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey

GPS-Spoofing Attack Detection Technology for UAVs Based on Kullback–Leibler Divergence

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