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There is a significant surge in unmanned aerial vehicles' (UAVs) popularity with an ongoing increase in demand due to their multi-functional uses. The capacity of the UAVs to respond to society and industry needs account for their pervasiveness. UAVs can be strategically deployed to enhance network efficiency and support critical missions, including vital tasks like infrastructure monitoring operations. To be effective, a UAV must interact securely with its network's entities, such as ground control stations, other UAVs, air traffic control systems, and navigation satellite systems. UAVs are exposed to a dangerous and costly world of cyber dangers as a result of cyber technology and connections. The UAV and the Ground Control Station (GCS) exchange information using communication lines, which are vulnerable to cyber-attacks. As a result, detective, defensive, and preventative countermeasures are critical. The Micro Air Vehicle Link (MAVLink) protocol is a widely used lightweight communication protocol for enabling communication between UAVs and GCSs. It outlines communication exchanges between a GCS and a UAV. It carries information about the UAV's condition as well as commands for control from the GCS. Although widely used, the MAVLink protocol lacks sufficient security measures and is susceptible to various types of attacks, which can pose significant risks to public safety. UAV data should be encrypted because they are very sensitive. To give a high level of security, complex operation encryption techniques are developed. These mathematical operations bring forth issues with efficiency and power usage, though. One of these methods, the ChaCha cipher, recently gained popularity after Google used it in several applications. In the current study, a new stream cipher method with low-duty cycles is proposed for protecting data in UAVs and is compared with existing security algorithms on the basis of various factors. The research findings show that by including the suggested method in MAVLink, it is possible to maintain both message secrecy and battery life for a resource-constrained UAV. This can be done while using comparable amounts of memory and CPU and without affecting MAVLink's performance. [ABSTRACT FROM AUTHOR]

Aerial vehicle networks (AVNs) compose a large number of heterogeneous aerial nodes, such as unmanned aerial vehicles, aircrafts and helicopters. The main characteristics of these networks are the high mobility of aerial nodes and the dynamic network topology. AVNs represent attractive targets for attackers due to the fact that aerial nodes could be connected to an untrusted network and hence lead the attackers to launch lethal threats, e.g., aircraft crash. Therefore, the security of AVNs is mandatory. In this article, we examine the challenges of cyber detection methods to secure AVNs and review exiting security schemes proposed in the current literature. Furthermore, we propose a security framework to protect an aircraft (SFA) against malicious behaviors that target aircrafts communication systems. Numerical results show that SFA achieves a high accuracy detection and prediction rates as compared to the current intrusion detection for aircrafts communication system. [ABSTRACT FROM AUTHOR]

Methods for maintaining separation between aircraft in the current airspace system rely heavily on human operators. A conflict is an event in which two or more aircraft experience a loss of minimum allowable separation. Interest has grown in developing more advanced automation tools to predict when a traffic conflict is going to occur and to assist in its resolution. The term air space deconfliction is used to describe the resolution of conflicts after they have been predicted or detected. Due to the computationally intensive character of conflict detection and airspace deconfliction, as well as their data parallel nature, they are naturally amenable to parallel processing. This work discusses a parallel implementation of a conflict detection algorithm for application to airspace deconfliction. It uses the NVIDIA Quadro FX 5800 and the Tesla C1060 graphical processing units (GPUs) in conjunction with the Compute Unified Device Architecture (CUDA) hardware/software architecture. Details of the implementation are discussed, including the use of streams for asynchronous programming and the use of multiple GPUs. The performance of the parallel implementation is compared to that of an equivalent sequential version and shown to exhibit improvement in execution time. Recommendations are provided to further improve performance of the algorithm. [ABSTRACT FROM AUTHOR]