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1. Compromising Flight Paths of Autopiloted Drones

   https://doi.org/10.1109/ICUAS.2019.8798162  

   Chen, W; Dong, Y; Duan, Z (June 2019, 2019 International Conference on Unmanned Aircraft Systems (ICUAS))

   While more and more consumer drones are abused in recent attacks, there is still very little systematical research on countering malicious consumer drones. In this paper, we focus on this issue and develop effective attacks to common autopilot control algorithms to compromise the flight paths of autopiloted drones, e.g., leading them away from its preset paths. We consider attacking an autopiloted drone in three phases: attacking its onboard sensors, attacking its state estimation, and attacking its autopilot algorithms. Several firstphase attacks have been developed (e.g., [1]–[4]); second-phase attacks (including our previous work [5], [6]) have also been investigated. In this paper, we focus on the third-phase attacks. We examine three common autopilot algorithms, and design several attacks by exploiting their weaknesses to mislead a drone from its preset path to a manipulated path. We present the formal analysis of the scope of such manipulated paths. We further discuss how to apply the proposed attacks to disrupt preset drone missions, such as missing a target in searching an area or misleading a drone to intercept another drone, etc. Many potential attacks can be built on top of the proposed attacks. We are currently investigating different models to apply such attacks on common drone missions and also building prototype systems on ArduPilot for real world tests. We will further investigate countermeasures to address the potential damages. 

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2. Manipulating Drone Position Control

   https://doi.org/10.1109/CNS.2019.8802817  

   Chen, Wenxin; Dong, Yingfei; Duan, Zhenhai (June 2019, 2019 IEEE Conference on Communications and Network Security (CNS))

   Although consumer drones have been used in many attacks, besides specific methods such as jamming, very little research has been conducted on systematical methods to counter these drones. In this paper, we develop generic methods to compromise drone position control algorithms in order to make malicious drones deviate from their targets. Taking advantage of existing methods to remotely manipulate drone sensors through cyber or physical attacks (e.g., [1], [2]), we exploited the weaknesses of position estimation and autopilot controller algorithms on consumer drones in the proposed attacks. For compromising drone position control, we first designed two state estimation attacks: a maximum False Data Injection (FDI) attack and a generic FDI attack that compromised the Kalman-Filter-based position estimation (arguably the most popular method). Furthermore, based on the above attacks, we proposed two attacks on autopilot-based navigation, to compromise the actual position of a malicious drone. To the best of our knowledge, this is the first piece of work in this area. Our analysis and simulation results show that the proposed attacks can significantly affect the position estimation and the actual positions of drones. We also proposed potential countermeasures to address these attacks. 

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3. Human-Centered Drone LED Illumination System: A Proof-of-Concept Prototype

   https://doi.org/10.1177/1071181321651124  

   Hao, Shuai; Yerebakan, Mustafa Ozkan; Hu, Boyi (September 2021, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Recent years have seen a rapid increase in drone usage in both commercial and personal use, due to recent changes in guidelines by the Federal Aviation Administration (FAA). In those guidelines, however, there seems to be very few requirements in terms of illumination requirements, apart from the need to use a visible strob ing anti-collision light for nighttime operations. Hence in this study, wereviewed existing LED illumination systems in off-the-shelf drones to determine what type of configurations they have and how is the LED illumination system generally used. We also introduced a customizable LED illumination system and tested it in a human in the loop study. Our p reliminary findings have revealed that the colors that are preferred by the participants did not match the most used colors in existing LED illumination systems in most off-the-shelf drones. We also observed a possible relationship between the color preferred and the weather conditions. 

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4. Implementation of an Artificial Immune System to Mitigate Cybersecurity Threats in Unmanned Aerial Systems

   https://doi.org/10.1109/ICII.2019.00013  

   Shivers, Meagan; Llanes, Christian; Sherman, Maxwell (November 2019, IEEE International Workshop on Sensing, Communication, and Control for Unmanned Aerial Systems)

   he pervasive operation of customer drones, or small-scale unmanned aerial vehicles (UAVs), has raised serious concerns about their privacy threats to the public. In recent years, privacy invasion events caused by customer drones have been frequently reported. Given such a fact, timely detection of invading drones has become an emerging task. Existing solutions using active radar, video or acoustic sensors are usually too costly (especially for individuals) or exhibit various constraints (e.g., requiring visual line of sight). Recent research on drone detection with passive RF signals provides an opportunity for low-cost deployment of drone detectors on commodity wireless devices. However, the state of the arts in this direction rely on line-of-sight (LOS) RF signals, which makes them only work under very constrained conditions. The support of more common scenarios, i.e., non-line-of-sight (NLOS), is still missing for low-cost solutions. In this paper, we propose a novel detection system for privacy invasion caused by customer drone. Our system is featured with accurate NLOS detection with low-cost hardware (under $50). By exploring and validating the relationship between drone motions and RF signal under the NLOS condition, we find that RF signatures of drones are somewhat “amplified” by multipaths in NLOS. Based on this observation, we design a two-step solution which first classifies received RSS measurements into LOS and NLOS categories; deep learning is then used to extract the signatures and ultimately detect the drones. Our experimental results show that LOS and NLOS signals can be identified at accuracy rates of 98.4% and 96% respectively. Our drone detection rate for NLOS condition is above 97% with a system implemented using Raspberry PI 3 B+. 

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5. Two Supervised Machine Learning Approaches for Wind Velocity Estimation Using Multi-Rotor Copter Attitude Measurements

   https://doi.org/10.3390/s20195638  

   Crowe, David; Pamula, Raghava; Cheung, Hing Yuet; De Wekker, Stephan F. (October 2020, Sensors)

   null (Ed.)

   In this work we address the adequacy of two machine learning methods to tackle the problem of wind velocity estimation in the lowermost region of the atmosphere using on-board inertial drone data within an outdoor setting. We fed these data, and accompanying wind tower measurements, into a K-nearest neighbor (KNN) algorithm and a long short-term memory (LSTM) neural network to predict future windspeeds, by exploiting the stabilization response of two hovering drones in a wind field. Of the two approaches, we found that LSTM proved to be the most capable supervised learning model during more capricious wind conditions, and made competent windspeed predictions with an average root mean square error of 0.61 m·s−1 averaged across two drones, when trained on at least 20 min of flight data. During calmer conditions, a linear regression model demonstrated acceptable performance, but under more variable wind regimes the LSTM performed considerably better than the linear model, and generally comparable to more sophisticated methods. Our approach departs from other multi-rotor-based windspeed estimation schemes by circumventing the use of complex and specific dynamic models, to instead directly learn the relationship between drone attitude and fluctuating windspeeds. This exhibits utility in a range of otherwise prohibitive environments, like mountainous terrain or off-shore sites. 

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