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.
Finally, authors in @cite_19 analyze the basic architecture of a drone and propose a generic drone forensic model that would improve the digital investigation process. They also provide recommendations on how one should perform forensics on the various components of a drone such as camera and Wi-Fi.
@cite_19
2606197117 2963054668 2889328478 255486541
Ease of availability and affordability of unmanned aerial vehicles (UAV) have led to an increase in its popularity amongst the public. The proliferation of UAVs has also augmented several security issues. These devices are used for illegal activities such as drug smuggling and privacy invasion. The purpose of this research paper is to analyze the basic architecture of a drone, and to propose a generic drone forensic model that would improve the digital investigation process. This paper also provides recommendations on how one should perform forensics on the various components of a drone such as camera and Wi-Fi. Drones equipped with cameras are emerging as a powerful tool for large-scale aerial 3D scanning, but existing automatic flight planners do not exploit all available information about the scene, and can therefore produce inaccurate and incomplete 3D models. We present an automatic method to generate drone trajectories, such that the imagery acquired during the flight will later produce a high-fidelity 3D model. Our method uses a coarse estimate of the scene geometry to plan camera trajectories that: (1) cover the scene as thoroughly as possible; (2) encourage observations of scene geometry from a diverse set of viewing angles; (3) avoid obstacles; and (4) respect a user-specified flight time budget. Our method relies on a mathematical model of scene coverage that exhibits an intuitive diminishing returns property known as submodularity. We leverage this property extensively to design a trajectory planning algorithm that reasons globally about the non-additive coverage reward obtained across a trajectory, jointly with the cost of traveling between views. We evaluate our method by using it to scan three large outdoor scenes, and we perform a quantitative evaluation using a photorealistic video game simulator. To better address the growing threats of small commercial Unmanned Aerial Systems (UASs), we developed novel methods to identify specific types of drone models by building their wireless communication profiles with machine learning algorithms. In this paper, we present a basic framework for identifying unique features and discuss basic methods for several drone models. The results based on our proposed methods demonstrate their effectiveness. We continue to collect experimental results involving several popular drones and investigate more features to enhance the accuracy of our methods. We also outline our future work involving additional drone models and further methods used to improve identification accuracy. Nowadays, forensic analysis of digital images is especially important, given the frequent use of digital cameras in mobile devices. The identification of the device type or the make and model of image source are two important branches of forensic analysis of digital images. In this paper we have addressed both of these, with an approach based on different types of image features and the classification using support vector machines. The study has mainly focused on images created with mobile devices and as a result, the techniques and features have been adapted or created for this purpose. There have been a total of 36 experiments classified into 5 sets, in order to test different configurations of the techniques. In the configuration of the experiments, the future use of the technique by the forensic analyst in real situations to create experiments with high technical requirements was taken into account, amongst other things.