How to Analyze the Cyber Threat from Drones: Background, Analysis Frameworks, and Analysis Tools

Best, Katharina Ley; Schmid, Jon; Tierney, Shane; Awan, Jalal; Beyene, Nahom M.; Holliday, Maynard A.; Khan, Raza; Lee, Karen

doi:10.7249/rr2972

Cited by 18 publications

(8 citation statements)

References 9 publications

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“…This will represent tens of millions of drone owners around the world. Unfortunately, this growing use of drones (in particular for recreational activities) raises concerns about privacy, but also security of sensitive areas such as airports, prisons, and industrial and military facilities [3]- [5]. Although in many countries bills are passed and legislation is updated to regulate drone activities [1], [5], [6], it is very likely that an untrained pilot does not know the legislation, and a motivated criminal simply does not care.…”

Section: Introductionmentioning

confidence: 99%

Extracting Unambiguous Drone Signature Using High-Speed Camera

et al. 2022

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In recent years, the use of drones for recreational and commercial activities has grown rapidly due to their affordability and performance. This growing use raises concerns about the threats drones pose to the security of sensitive areas such as airports, prisons, industrial and military facilities. In response to these threats, drones detection methods are being actively developed. In particular, most camera-based methods rely on appearance to perform detection. They are therefore prone to error due to the great similarity between drones and some other flying entities such as birds. However, from a kinematic perspective, unlike birds, drones, especially multicopters, have a propeller rotation speed. The method proposed in this paper uses the propeller rotation speed as the key physical parameter on which to rely to unambiguously distinguish drones from other flying entities. The basic idea consists in using discrete Fourier transform to determine the propellers rotation speed from high frame rate videos, and extracting the propellers induced drone signature as a quantitative camera-based drone signature. The proposed algorithm proceeds as follows: flying entities are continuously tracked in the sky; discrete Fourier transform, applied to the video stream within a time window ending at the current instant (frame), is used to extract the propellers induced drone signature which unambiguously confirm each flying entity as being a drone or not. Experimental results obtained using a consumer-grade camera at a frame rate of 240Hz demonstrate the effectiveness and reliability of the proposed method.INDEX TERMS Drone detection, discrete Fourier transform, high speed camera, propellers induced drone signature, propeller rotation speed, unambiguous drone signature.

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Section: Introductionmentioning

confidence: 99%

Extracting Unambiguous Drone Signature Using High-Speed Camera

et al. 2022

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“…This may include the hardware and software running on the UAV, the Ground Control Station (GCS), the support Cloud Services (CS), and the connections between these entities. Cyberthreats are further accentuated by UAVs connecting to open, wireless networks with access to the Internet [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…Other known attacks to the UAS include computer virus infection of the mission command center of a US air force base controlling military UAVs [5]. Best et al [3] introduced a framework for understanding and documenting vulnerabilities and attack opportunities of the UAS. It was This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 861111.…”

Section: Introductionmentioning

confidence: 99%

Security Threats Analysis of the Unmanned Aerial Vehicle System

Jacobsen

Marandi

2021

MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)

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Autonomous Unmanned Aerial Vehicles (UAVs) find increasing use in the civil airspace where multi-UAV systems are deployed to perform operations supervised by remote control facilities. Since these multi-UAV systems operate in public locations and connect using open communication standards, it raises significant security concerns. While most UAV designers befittingly deal with the complexity of autonomy, communication, and control, they often ignore to consider system security aspects in the early design phase. To support the security design process of multi-UAV systems, this paper provides an analysis of cybersecurity threats of multi-UAV systems based on the STRIDE model. The identified threats are subsequently linked with a risk assessment to be prioritized according to severity. We conclude by establishing a set of security design recommendations for connected inspection UAVs to contribute with guidance for the design of future multi-UAV systems.

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“…agriculture monitoring [8] and human action recognition [1,2] in the imagery obtained through drones. In addition to detecting different objects from a drone video, it is also important to detect the drone itself from a video captured by another drone to avoid drone attacks [3], drone collisions [33] and safe multi-drone flights [24,32]. Detection of ground objects and aerial drones from drone videos is a very challenging problem due to a large and abrupt camera motion, arbitrary drone shape and view changes, occlusion, and more importantly small object size.…”

Section: Introductionmentioning

confidence: 99%

Dogfight: Detecting Drones from Drones Videos

Ashraf¹,

Sultani²,

Shah³

2021

Preprint

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As airborne vehicles are becoming more autonomous and ubiquitous, it has become vital to develop the capability to detect the objects in their surroundings. This paper attempts to address the problem of drones detection from other flying drones. The erratic movement of the source and target drones, small size, arbitrary shape, large intensity variations, and occlusion make this problem quite challenging. In this scenario, region-proposal based methods are not able to capture sufficient discriminative foregroundbackground information. Also, due to the extremely small size and complex motion of the source and target drones, feature aggregation based methods are unable to perform well. To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach employing spatio-temporal attention cues. During the first stage, given the overlapping frame regions, detailed contextual information is captured over convolution feature maps using pyramid pooling. After that pixel and channel-wise attention is enforced on the feature maps to ensure accurate drone localization. In the second stage, first stage detections are verified and new probable drone locations are explored. To discover new drone locations, motion boundaries are used. This is followed by tracking candidate drone detections for a few frames, cuboid formation, extraction of the 3D convolution feature map, and drones detection within each cuboid. The proposed approach is evaluated on two publicly available drone detection datasets and outperforms several competitive baselines.

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How to Analyze the Cyber Threat from Drones: Background, Analysis Frameworks, and Analysis Tools

Abstract: How to Analyze the Cyber Threat from DronesBackground, Analysis Frameworks, and

Cited by 18 publications

References 9 publications

Extracting Unambiguous Drone Signature Using High-Speed Camera

Extracting Unambiguous Drone Signature Using High-Speed Camera

Security Threats Analysis of the Unmanned Aerial Vehicle System

Dogfight: Detecting Drones from Drones Videos

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