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., , ), 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.
Accurately Redirecting a Malicious Drone
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.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- IEEE Consumer Communications and Networking Conference (CCNC)
- Sponsoring Org:
- National Science Foundation
More Like this
Location information is critical to a wide variety of navigation and tracking applications. GPS, today's de-facto outdoor localization system has been shown to be vulnerable to signal spoofing attacks. Inertial Navigation Systems (INS) are emerging as a popular complementary system, especially in road transportation systems as they enable improved navigation and tracking as well as offer resilience to wireless signals spoofing and jamming attacks. In this paper, we evaluate the security guarantees of INS-aided GPS tracking and navigation for road transportation systems. We consider an adversary required to travel from a source location to a destination and monitored by an INS-aided GPS system. The goal of the adversary is to travel to alternate locations without being detected. We develop and evaluate algorithms that achieve this goal, providing the adversary significant latitude. Our algorithms build a graph model for a given road network and enable us to derive potential destinations an attacker can reach without raising alarms even with the INS-aided GPS tracking and navigation system. The algorithms render the gyroscope and accelerometer sensors useless as they generate road trajectories indistinguishable from plausible paths (both in terms of turn angles and roads curvature). We also design, build and demonstrate that themore »
Small-scale unmanned aerial vehicles (UAVs) have become an increased presence in recent years due to their decreasing price and ease of use. Similarly, ways to detect drones through easily accessible programs like WireShark have raised more potential threats, including an increase in ease of jamming and spoofing drones utilizing commercially of the shelf (COTS) equipment like software defined radio (SDR). Given these advancements, an active area of research is drone security. Recent research has focused on using a HackRF SDR to perform eavesdropping or jamming attacks; however, most have failed to show a proposed remediation. Similarly, many research papers show post analysis of communications, but seem to lack a conclusive demonstration of command manipulation. Our security assessment shows clear steps in the manipulation of a WiFi drone using the aircrack-ng suite without the need for additional equipment like a SDR. This shows that anyone with access to a computer could potentially take down a drone. Alarmingly, we found that the COTS WiFi drone in our experiment still lacked the simple security measure of a password, and were very easily able to take over the drone in a deauthorization attack. We include a proposed remediation to mitigate the preformed attack andmore »
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., –); second-phase attacks (including our previous work , ) 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 onmore »
Implementation of an Artificial Immune System to Mitigate Cybersecurity Threats in Unmanned Aerial Systemshe 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 thismore »