We propose PiNcH, a methodology to detect the presence of a drone and its current status leveraging just the communication traffic exchanged between the drone and its Remote Controller (RC). PiNcH is built applying standard classification algorithms to the eavesdropped traffic, analyzing features such as packets inter-arrival time and size. PiNcH does not require either any special hardware or to transmit any signal. Indeed, it is fully passive and it resorts to cheap and general purpose hardware. To evaluate the effectiveness of our solution, we collected real communication measurements from the 3DR SOLO drone, being the most popular open-source hardware, running the widespread ArduCopter open-source firmware, mounted on-board on a wide range of commercial amateur drones. Then, we test our solution against different publicly available wireless traces. The results prove that PiNcH can efficiently and effectively: (i) identify the presence of the drone in several heterogeneous scenarios; (ii) identify the current state of a powered-on drone, i.e., flying or lying on the ground; (iii) discriminate the movement of the drone; and, finally, (iv) estimate a lower bound on the time required to identify a drone with the requested level of assurance. The quality and viability of our solution do prove that network traffic analysis can be successfully adopted for drone identification and pave the way for future research in the area.
Machine learning techniques have been successfully used for other purposes in this research field. In @cite_21 , authors proposed a wireless power transfer system that predicts the drone's behavior based on the flight data, utilizing machine learning techniques and Naive Bayes algorithms.
@cite_21
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In this paper, the design of a novel wireless power transfer system utilizing drones with machine learning techniques is presented. Research on drones is currently a fast growing field with a great potential in many ubiquitous applications. The wireless power transfer system with the fixed operation frequency at 13.56MHz is applied to an 1-coil receiver on the drone, with an array of transmitter coils on the ground. This work presents an approach where the data is considered “classified” using machine learning techniques, which allows the accurate prediction of the drone's position, thus enhancing the wireless power transfer efficiency. Mobile wireless sensor networks have been extensively deployed for enhancing environmental monitoring and surveillance. The availability of low-cost mobile robots equipped with a variety of sensors makes them promising in target coverage tasks. They are particularly suitable where quick, inexpensive, or nonlasting visual sensing solutions are required. In this paper, we consider the problem of low complexity target tracking to cover and follow moving targets using flying robots. We tackle this problem by clustering targets while estimating the camera location and orientation for each cluster separately through a cover-set coverage method. We also leverage partial knowledge of target mobility to enhance the efficiency of our proposed algorithms. Three computationally efficient approaches are developed: predictive fuzzy , predictive incremental fuzzy , and local incremental fuzzy . The objective is to find a compromise among coverage efficiency, traveled distance, number of drones required, and complexity. The targets move according to one of the following three possible mobility patterns: random waypoint, Manhattan grid, and reference point group mobility patterns. The feasibility of our algorithms and their performance are also tested on a real-world indoor testbed called drone-be-gone , using Parrot AR.Drone quadcopters. The deployment confirms the results obtained with simulations and highlights the suitability of the proposed solutions for real-time applications. Analysis of interactions with remotely controlled devices has been used to detect the onset of hijacking attacks, as well as for forensics analysis, e.g., to identify the human controller. Its effectiveness is known to depend on the remote device type as well as on the properties of the remote control signal. This paper shows that the radio control signal sent to an unmanned aerial vehicle (UAV) using a typical transmitter can be captured and analyzed to identify the controlling pilot using machine learning techniques. Twenty trained pilots have been asked to fly a high-end research drone through three different trajectories. Control data have been collected and used to train multiple classifiers. Best performance has been achieved by a random forest classifier that achieved accuracy around 90 using simple time-domain features. Extensive tests have shown that the classification accuracy depends on the flight trajectory and that the pitch, roll, yaw, and thrust control signals show different levels of significance for pilot identification. This result paves the way to a number of security and forensics applications, including continuous identification of UAV pilots to mitigate the risk of hijacking. For some time now machine learning methods have been widely used in perception for autonomous robots. While there have been many results describing the performance of machine learning techniques with regards to their accuracy or convergence rates, relatively little work has been done on developing theoretical performance guarantees about their stability and robustness. As a result, many machine learning techniques are still limited to being used in situations where safety and robustness are not critical for success. One way to overcome this difficulty is by using reachability analysis, which can be used to compute regions of the state space, known as reachable sets, from which the system can be guaranteed to remain safe over some time horizon regardless of the disturbances. In this paper we show how reachability analysis can be combined with machine learning in a scenario in which an aerial robot is attempting to learn the dynamics of a ground vehicle using a camera with a limited field of view. The resulting simulation data shows that by combining these two paradigms, one can create robotic systems that feature the best qualities of each, namely high performance and guaranteed safety.