AliDrone: Enabling Trustworthy Proof-of-Alibi for Commercial Drone Compliance

Tianyuan, Liu; Hojjati, Avesta; Bates, Adam; Nahrstedt, Klara

doi:10.1109/icdcs.2018.00086

Cited by 19 publications

(20 citation statements)

References 21 publications

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“…ARM partnered with GlobalPlatform and has defined new TEE APIs [15]. TrustZone encompasses the following major features [16]: (a) safe and secure boot (to ensure all software components are in a trusted state before launching the OS); (b) isolated execution of critical applications (i.e., in a secure enclave) and (c) protection for trusted applications data (in terms of integrity and confidentiality).…”

Section: Preliminariesmentioning

confidence: 99%

“…We implemented a proof-of-concept prototype of Contego-TEE on Raspberry Pi 3 (RPi3) Model B [7] (equipped with 1.2 GHz 64-bit ARMv8 CPU and 1 GB RAM). We selected RPi3 as our implementation platform since (a) it supports ARM TrustZone and (b) previous research has shown feasibility of deploying multiple IoT-specific applications on RPi3 [16,19,[29][30][31]. We developed Contego-TEE using the Open-Portable Trusted Execution Environment (OP-TEE) [32] software stack that uses GlobalPlatform TEE APIs [15] to provide TrustZone functionality.…”

Section: System Implementationmentioning

confidence: 99%

See 1 more Smart Citation

Protecting Actuators in Safety-Critical IoT Systems from Control Spoofing Attacks

Hasan

Mohan

2019

Proceedings of the 2nd International ACM Workshop on Security and Privacy for the Internet-of-Things

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In this paper, we propose a framework called Contego-TEE to secure Internet-of-Things (IoT) edge devices with timing requirements from control spoofing attacks where an adversary sends malicious control signals to the actuators. We use a trusted computing base available in commodity processors (such as ARM TrustZone) and propose an invariant checking mechanism to ensure the security and safety of the physical system. A working prototype of Contego-TEE was developed using embedded Linux kernel. We demonstrate the feasibility of our approach for a robotic vehicle running on an ARM-based platform.

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Section: Preliminariesmentioning

confidence: 99%

Section: System Implementationmentioning

confidence: 99%

Protecting Actuators in Safety-Critical IoT Systems from Control Spoofing Attacks

Hasan

Mohan

2019

Proceedings of the 2nd International ACM Workshop on Security and Privacy for the Internet-of-Things

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“…• Log-GPS: The host may require the guest drone to only fly along pre-defined "drone lanes" when within its restricted space. The host can check this by requiring the drone to log its GPS feed during its stay in the restricted space, and to submit this GPS log for analysis as it leaves [20].…”

Section: Session 2: Security Privacy and Isolationmentioning

confidence: 99%

“…PROTC ensures that applications runnning on the drone are able to securely access its periperhals even when the operating system of the drone has been compromised. Liu et al [20] investigate the problem of ensuring a drone has not strayed into no-fly zones. To do this, they leverage the TrustZone to keep a tamper-proof log of the drone's GPS locations, which then provide an alibi to a third-party auditor that the drone was in compliance.…”

Section: Related Workmentioning

confidence: 99%

Regulating Drones in Restricted Spaces

Vijeev

Ganapathy

Bhattacharyya

2019

Proceedings of the 20th International Workshop on Mobile Computing Systems and Applications

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Commercial and end-user drones come equipped with a wide array of sensors. Unregulated use of such drones in public airspaces poses a serious threat to the privacy of citizens. We make the case for restricted spaces for drones, which are geographic areas for which a host can specify its privacy policies. Guest drones must prove to the host that they are in compliance with the host's policies before entering the restricted space. We then make the case for an information-flow control-based policy enforcement framework on drones, and sketch the design of a prototype framework atop the Robot Operating System (ROS). CCS CONCEPTS • Security and privacy → Mobile platform security; Information flow control;

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“…Wesson et al [11] prevented counterfeit navigation messages by combining signature-based security methods with hypothesis tests. In [12], a trusted execution environment was used to generate cryptographically signed GPS messages in order to prevent their forgery. Nonetheless, even though NMA techniques are considered a practical and effective defense against GPS spoofing attacks, those techniques induce significant computational cost and latency due to signature verification.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for GPS Spoofing Detection in Cellular-Enabled UAV Systems

Dang

Benzaïd

Yang

et al. 2021

2021 International Conference on Networking and Network Applications (NaNA)

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Cellular-based Unmanned Aerial Vehicle (UAV) systems are a promising paradigm to provide reliable and fast Beyond Visual Line of Sight (BVLoS) communication services for UAV operations. However, such systems are facing a serious GPS spoofing threat for UAV's position. To enable safe and secure UAV navigation BVLoS, this paper proposes a cellular network assisted UAV position monitoring and anti-GPS spoofing system, where deep learning approach is used to live detect spoofed GPS positions. Specifically, the proposed system introduces a MultiLayer Perceptron (MLP) model which is trained on the statistical properties of path loss measurements collected from nearby base stations to decide the authenticity of the GPS position.Experiment results indicate the accuracy rate of detecting GPS spoofing under our proposed approach is more than 93% with three base stations and it can also reach 80% with only one base station.

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AliDrone: Enabling Trustworthy Proof-of-Alibi for Commercial Drone Compliance

Cited by 19 publications

References 21 publications

Protecting Actuators in Safety-Critical IoT Systems from Control Spoofing Attacks

Protecting Actuators in Safety-Critical IoT Systems from Control Spoofing Attacks

Regulating Drones in Restricted Spaces

Deep Learning for GPS Spoofing Detection in Cellular-Enabled UAV Systems

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