More Like this

1. 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. 

   more » « less
2. Accurately Redirecting a Malicious Drone

   Chen, W.; Dong, Y.; Duan, Z. (January 2022, IEEE Consumer Communications and Networking Conference (CCNC))

   Although some existing counterdrone measures can disrupt the invasion of certain consumer drone, to the best of our knowledge, none of them can accurately redirect it to a given location for defense. In this paper, we proposed a Drone Position Manipulation (DPM) attack to address this issue by utilizing the vulnerabilities of control and navigation algorithms used on consumer drones. As such drones usually depend on GPS for autopiloting, we carefully spoof GPS signals based on where we want to redirect a drone to, such that we indirectly affect its position estimates that are used by its navigation algorithm. By carefully manipulating these states, we make a drone gradually move to a path based on our requirements. This unique attack exploits the entire stack of sensing, state estimation, and navigation control together for quantitative manipulation of flight paths, different from all existing methods. In addition, we have formally analyzed the feasible range of redirected destinations for a given target. Our evaluation on open-source ArduPilot system shows that DPM is able to not only accurately lead a drone to a redirected destination but also achieve a large redirection range. 

   more » « less
3. Efficient Authentication of Drones to mmWave Wireless Mesh Networks in Post-Disaster Scenarios

   https://doi.org/10.1109/GLOBECOM42002.2020.9322282  

   Abdel-Malek, Mai A.; Akkaya, Kemal; Saputro, Nico; Ibrahim, Ahmed S. (December 2020, GLOBECOM 2020 - 2020 IEEE Global Communications Conference)

   null (Ed.)

   Unmanned Aerial Vehicles (UAVs), or drones, are increasingly being utilized for public safety circumstances including post-disaster recovery of destroyed communication infrastructure. For instance, drones are temporarily positioned within an affected area to create a wireless mesh network among public safety personnel. To serve the need for high-rate video-based damage assessment, drone-assisted communication can utilize high- bandwidth millimeter wave (mmWave) technologies such as IEEE 802.11ad. However, short-range mmWave communication makes it hard for optimally- positioned drones to be authenticated with a centralized network control center. Therefore and assuming that there are potential imposters, we propose two lightweight and fast authentication mechanisms that take into account the physical limitations of mmWave communication. First, we propose a drone-to-drone authentication mechanism, which is based on proxy signatures from a control center. Accordingly, any newly joining drone can authenticate itself to an exist one rather than attempting to authenticate to the outof-reach control center. Second, we propose a drone-to- ground authentication mechanism, to enable each drone to authenticate itself to its associated ground users. Such authentication approach is based on challenge-response broadcast type, and it is still utilizing fast proxy signature approach. The evaluation of the proposed authentication mechanisms, conducted using NS-3 implementation of IEEE 802.11ad protocol, show their efficiency and practicality. 

   more » « less
4. Detection and Tracking of Drone Swarms using LiDAR

   Abir, Tasnim; Le, Vu; Kuantama, Endrowednes; Gupta, Pranjol; Copley, Austin; Dawes, Judith; Islam, Mohammad; Han, Richard; Nguyen, Phuc (June 2025, ACM MobiSys 2025)

   This paper introduces LiSWARM, a low-cost LiDAR system to detect and track individual drones in a large swarm. LiSWARM provides robust and precise localization and recognition of drones in 3D space, which is not possible with state-of-the-art drone tracking systems that rely on radio-frequency (RF), acoustic, or RGB image signatures. It includes (1) an efficient data processing pipeline to process the point clouds, (2) robust priority-aware clustering algorithms to isolate swarm data from the background, (3) a reliable neural network-based algorithm to recognize the drones, and (4) a technique to track the trajectory of every drone in the swarm. We develop the LiSWARM prototype and validate it through both in-lab and field experiments. Notably, we measure its performance during two drone light shows involving 150 and 500 drones and confirm that the system achieves up to 98% accuracy in recognizing drones and reliably tracking drone trajectories. To evaluate the scalability of LiSWARM, we conduct a thorough analysis to benchmark the system’s performance with a swarm consisting of 15,000 drones. The results demonstrate the potential to leverage LiSWARM for other applications, such as battlefield operations, errant drone detection, and securing sensitive areas such as airports and prisons. 

   more » « less
5. 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. 

   more » « less