ISEP - Instituto Superior de Engenharia do Porto
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Browsing ISEP - Instituto Superior de Engenharia do Porto by Author "Abbes, Tarek"
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- MAVSec: Securing the MAVLink Protocol for Ardupilot/PX4 Unmanned Aerial SystemsPublication . Allouch, Azza; Cheikhrouhou, Omar; Koubaa, Anis; Khalgui, Mohamed; Abbes, TarekThe MAVLink is a lightweight communication protocol between Unmanned Aerial Vehicles (UAVs) and ground control stations (GCSs). It defines a set of bi-directional messages exchanged between a UAV (aka drone) and a ground station. The messages carry out information about the UAV’s states and control commands sent from the ground station. However, the MAVLink protocol is not secure and has several vulnerabilities to different attacks that result in critical threats and safety concerns. Very few studies provided solutions to this problem. In this paper, we discuss the security vulnerabilities of the MAVLink protocol and propose MAVSec, a security-integrated mechanism for MAVLink that leverages the use of encryption algorithms to ensure the protection of exchanged MAVLink messages between UAVs and GCSs. To validate MAVSec, we implemented it in Ardupilot and evaluated the performance of different encryption algorithms (i.e. AES-CBC, AES-CTR, RC4 and ChaCha20) in terms of memory usage and CPU consumption. The experimental results show that ChaCha20 has a better performance and is more efficient than other encryption algorithms. Integrating ChaCha20 into MAVLink can guarantee its messages confidentiality, without affecting its performance, while occupying less memory and CPU consumption, thus, preserving memory and saving the battery for the resource-constrained drone
- Qualitative and Quantitative Risk Analysis and Safety Assessment of Unmanned Aerial Vehicles Missions Over the InternetPublication . Allouch, Azza; Koubaa, Anis; KHALGUI, Mohamed; Abbes, TarekIn the last few years, unmanned aerial vehicles (UAVs) are making a revolution as an emerging technology with many different applications in the military, civilian, and commercial elds. The advent of autonomous drones has initiated serious challenges, including how to maintain their safe operation during their missions. The safe operation of UAVs remains an open and sensitive issue since any unexpected behavior of the drone or any hazard would lead to potential risks that might be very severe. The motivation behind this work is to propose a methodology for the safety assurance of drones over the Internet (Internet of drones (IoD)). Two approaches will be used in performing the safety analysis: (1) a qualitative safety analysis approach and (2) a quantitative safety analysis approach. The rst approach uses the international safety standards, namely, ISO 12100 and ISO 13849 to assess the safety of drone's missions by focusing on qualitative assessment techniques. The methodology starts with hazard identi cation, risk assessment, risk mitigation, and nally draws the safety recommendations associated with a drone delivery use case. The second approach presents a method for the quantitative safety assessment using Bayesian networks (BN) for probabilistic modeling. BN utilizes the information provided by the rst approach to model the safety risks related to UAVs' ights. An illustrative UAV crash scenario is presented as a case study, followed by scenario analysis, to demonstrate the applicability of the proposed approach. These two analyses, qualitative and quantitative, enable all involved stakeholders to detect, explore, and address the risks of UAV ights, which will help the industry to better manage the safety concerns of UAVs.
- RoadSense: Smartphone Application to Estimate Road Conditions using Accelerometer and GyroscopePublication . Allouch, Azza; Koubâa, Anis; Abbes, Tarek; Ammar, AdelMonitoring the road condition has acquired a critical significance during recent years. There are different reasons behind broadening research on this field: to start with, it will guarantee safety and comfort to different road users; second, smooth streets will cause less damage to the car. Our motivation is to create a real-time Android Application RoadSense that automatically predicts the quality of the road based on tri-axial accelerometer and gyroscope, show the road location trace on a geographic map using GPS and save all recorded workout entries. C4.5 Decision tree classifier is applied on training data to classify road segments and to build our model. Our experimental results show consistent accuracy of 98.6%. Using this approach, we expect to visualize a road quality map of a selected region. Hence, we can provide constructive feedback to drivers and local authorities. Besides, Road Manager can benefit from this system to evaluate the state of their road network and make a checkup on road construction projects, whether they meet or not the required quality.