Realization of an Autonomous, Air-to-Air Counter Unmanned Aerial System (CUAS)

Goppert, James; Wagoner, Amy R.; Schrader, Daniel K.; Ghose, Shiva; Kim, Yong-Ho; Seongha, Park; Gómez, Mauricio; Matson, Eric T.; Hopmeier, Michael

doi:10.1109/irc.2017.10

Cited by 14 publications

(4 citation statements)

References 4 publications

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“…However, this distance is usually not sufficient for the non-autonomous reaction procedures (a UAV with a flight speed of about 10–15 ms -1 can pass this distance in 20–30 s). For this reason, we recommend a C-UAV approach and the implementation of autonomous defense systems with a maximum reaction time of 20 s [42,43].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Multispectral Detection of Commercial Unmanned Aerial Vehicles

Farlik

Krátký

Časar

et al. 2019

Sensors

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The fight against unmanned vehicles is nothing new; however, especially with the arrival of new technologies that are easily accessible for the wider population, new problems are arising. The deployment of small unmanned aerial vehicles (UAVs) by paramilitary organizations during conflicts around the world has become a reality, non-lethal “paparazzi” actions have become a common practice, and it is only a matter of time until the population faces lethal attacks. The basic prerequisite for direct defense against attacking UAVs is their detection. The authors of this paper analysed the possibility of detecting flying aircraft in several different electro-magnetic spectrum bands. Firstly, methods based on calculations and simulations were chosen, and experiments in laboratories and measurements of the exterior were subsequently performed. As a result, values of the radar cross section (RCS), the noise level, the surface temperature, and optical as well as acoustic traces of tested devices were quantified. The outputs obtained from calculated, simulated, and experimentally detected values were found via UAV detection distances using specific sensors working in corresponding parts of the frequency spectrum.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Multispectral Detection of Commercial Unmanned Aerial Vehicles

Farlik

Krátký

Časar

et al. 2019

Sensors

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“…At the last forensics step, the UAV caught by the countermeasures is analyzed, and further investigation is performed. Researchers at Purdue University realized an autonomous CUAS that intercepts an unauthorized UAV, as a result of a challenge put forward for researchers after the White House incident [6]. The system detects, tracks, and takes down class 1 UAVs using a hunter UAV attached with a net.…”

Section: Counter Unmanned Aerial Systemsmentioning

confidence: 99%

UAV Detection System with Multiple Acoustic Nodes Using Machine Learning Models

Matson

Yang

Smith

et al. 2019

2019 Third IEEE International Conference on Robotic Computing (IRC)

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“…This attack vector poses many challenges not only to the drone forensic process and examination, but also the detection, monitoring, and mitigation of attacks. Researchers in [3] proposed a fully autonomous system as a counter UAV solution supported with a net designed to safely capture the targeted UAS. Alternatively, lateral movement of drone attack can impact both the detection and IR processes if Command and Control (C&C) sent customized requests to erase or alter all digital evidence from Unmanned Aerial System (UAS).…”

Section: Introductionmentioning

confidence: 99%

A Comparative UAV Forensic Analysis: Static and Live Digital Evidence Traceability Challenges

Salamh

Karabiyik

Rogers

et al. 2021

Drones

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The raising accessibility of Unmanned Aerial Vehicles (UAVs), colloquially known as drones, is rapidly increasing. Recent studies have discussed challenges that may come in tow with the growing use of this technology. These studies note that in-depth examination is required, especially when addressing challenges that carry a high volume of software data between sensors, actuators, and control commands. This work underlines static and live digital evidence traceability challenges to further enhance the UAV incident response plan. To study the live UAV forensic traceability issues, we apply the `purple-teaming’ exercise on small UAVs while conducting UAV forensic examination to determine technical challenges related to data integrity and repeatability. In addition, this research highlights current static technical challenges that could pose more challenges in justifying the discovered digital evidence. Additionally, this study discusses potential drone anti-forensic techniques and their association with the type of use, environment, attack vector, and level of expertise. To this end, we propose the UAV Kill Chain and categorize the impact and complexity of all highlighted challenges based on the conducted examination and the presented scientific contribution in this work. To the best of our knowledge, there has not been any contribution that incorporates `Purple-Teaming’ tactics to evaluate UAV-related research in cybersecurity and digital forensics. This work also proposes a categorization model that classifies the discovered UAV static and live digital evidence challenges based on their complexity and impact levels

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Realization of an Autonomous, Air-to-Air Counter Unmanned Aerial System (CUAS)

Cited by 14 publications

References 4 publications

Multispectral Detection of Commercial Unmanned Aerial Vehicles

Multispectral Detection of Commercial Unmanned Aerial Vehicles

UAV Detection System with Multiple Acoustic Nodes Using Machine Learning Models

A Comparative UAV Forensic Analysis: Static and Live Digital Evidence Traceability Challenges

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