Secure Unmanned Aerial Vehicle Research Articles

Robust secure UAV relay-assisted cognitive communications with resource allocation and cooperative jamming

This paper considers a novel scenario, where a physical layer security issue is studied in unmanned aerial vehicles (UAVs)-assisted cognitive relay system. A secondary unmanned aerial vehicle (SUAV) relay delivers information from multiple secondary Internet of things (IoT) devices to a secondary user (SU) under the spectrum sharing with primary users (PUs). In the processing of the information transmission of the SUAV relay, a secondary eavesdropper (SE) wiretaps the information transmitted by the UAV relay with imperfect location information. In order to confuse the SE, a friendly SUAV jammer is employed to transmit jamming signals to the SE. To prevent the SE wiretapping information as much as possible, we aim to maximize average worst-case secrecy rate of the secondary relay network by jointly optimizing robust trajectories and power of the SUAV relay and jammer under the power, trajectories, information causality and multiple interference temperature (IT) threshold constraints. Thus, we formulate the original problem which is a challenging non-convex problem. We propose an effective algorithm to solve the original problem and attain locally optimal solution based on the successive convex approximation (SCA) technology and the alternate optimization method. Simulations are offered to demonstrate that our proposed resource allocation scheme can effectively improve the security performance of the SUAV relay network in comparison with other benchmark schemes.

Mobile Wireless Rechargeable UAV Networks: Challenges and Solutions

Unmanned aerial vehicles (UAVs) can help facilitate cost-effective and flexible service provisioning in future smart cities. Nevertheless, UAV applications generally suffer severe flight time limitations due to constrained onboard battery capacity, causing a necessity of frequent battery recharging or replacement when performing persistent missions. Utilizing wireless mobile chargers, such as vehicles with wireless charging equipment for on-demand self-recharging has been envisioned as a promising solution to address this issue. In this article, we present a comprehensive study of \underline{v}ehicle-assisted \underline{w}ireless rechargeable \underline{U}AV \underline{n}etworks (VWUNs) to promote on-demand, secure, and efficient UAV recharging services. Specifically, we first discuss the opportunities and challenges of deploying VWUNs and review state-of-the-art solutions in this field. We then propose a secure and privacy-preserving VWUN framework for UAVs and ground vehicles based on differential privacy (DP). Within this framework, an online double auction mechanism is developed for optimal charging scheduling, and a two-phase DP algorithm is devised to preserve the sensitive bidding and energy trading information of participants. Experimental results demonstrate that the proposed framework can effectively enhance charging efficiency and security. Finally, we outline promising directions for future research in this emerging field.

IRS-Assisted Secure UAV Transmission via Joint Trajectory and Beamforming Design

Despite the wide utilization of unmanned aerial vehicles (UAVs), UAV communications are susceptible to eavesdropping due to air-ground line-of-sight channels. Intelligent reflecting surface (IRS) is capable of reconfiguring the propagation environment, and thus is an attractive solution for integrating with UAV to facilitate the security in wireless networks. In this paper, we investigate the secure transmission design for an IRS-assisted UAV network in the presence of an eavesdropper. With the aim at maximizing the average secrecy rate, the trajectory of UAV, the transmit beamforming, and the phase shift of IRS are jointly optimized. To address this sophisticated problem, we decompose it into three sub-problems and resort to an iterative algorithm to solve them alternately. First, we derive the closed-form solution to the active beamforming. Then, with the optimal transmit beamforming, the passive beamforming optimization problem of fractional programming is transformed into corresponding parametric sub-problems. Moreover, the successive convex approximation is applied to deal with the non-convex UAV trajectory optimization problem by reformulating a convex problem which serves as a lower bound for the original one. Simulation results validate the effectiveness of the proposed scheme and the performance improvement achieved by the joint trajectory and beamforming design.

A Secure Unmanned Aerial Vehicle Service for Medical System to Improve Smart City Facilities

The use of drone technology and drones are currently widespread due to their increasing applications. However, there are some specific security-based challenges in the authentication process. In most drone-based applications, there are many authentication approaches, which are subject to handover delay issues with security complexities for an attack. To end these issues, the presented research has focused on developing a novel Optimized deep learning model known as Fruit Fly based UNet Drone Assisted Security (FFUDAS) to remove the malicious attacks. Moreover, the user requests are stored in the cloud, and the stored data are trained to the drones. Hereafter, the drones can deliver medicine to the requestor’s location; in that, the malicious attacks were changes the location of drones. Once the attack is identified, then the attack removal process is done. Finally, the new path location to the requested user was identified with the help of fruit fly fitness; then the medicines are delivered to the requested user’s location. Furthermore, the designed procedure is executed in an NS2 platform with required nodes. The robustness of the presented model was verified by evaluating the metrics like confidential data rate, execution time, handover delay, pack perception and data delivery rate, and energy consumption. Furthermore, to identify the effectiveness of the presented work, the presented model is compared with other existing schemes. The comparison results show that the presented model has higher throughput, less execution time and handover delay.

Joint trajectory and power optimization for mobile jammer-aided secure UAV relay network

This paper investigates the achievable average secrecy rate for a mobile jammer-aided secure unmanned aerial vehicle (UAV) relaying system. Specifically, one UAV is deployed as relay to support the communications between a source node and a destination node. An additional cooperative UAV is dispatched to enhance security by transmitting an the interference signal to interfere with any suspicious eavesdropping. With the objective of enhancing secrecy against eavesdropping, an optimization problem for jointly optimizing the time scheduling, transmit power, and UAV's trajectory is formulated while considering the energy-constrained condition of rotary-wing UAV. As the corresponding optimization problem is mixed-integer non-convex, we propose an iterative algorithm to efficiently solve this optimization problem by applying successive convex optimization and block coordinate descent techniques to derive a locally optimal solution. Simulation results demonstrate the effectiveness of the proposed algorithm compared with benchmark algorithms.

AerialBlocks: Blockchain-Enabled UAV Virtualization for Industrial IoT

Unmanned Aerial Vehicles (UAV) are emerging as a powerful tool in various industrial applications in the context of enabling Industrial IoT. The limited power source and flight time of UAVs along with the need for skilled UAV operators are some of the most daunting challenges to the integration of UAVs in industrial applications such as site inspection, workforce monitoring, logistics, and others. In this article, we propose the unique paradigm of AerialBlocks for blockchain.enabled UAV virtualization to provide virtual UAV-as-a-service for industrial applications. AerialBlocks also aims at providing secure and persistent UAV services to the end-users along with a partially decentralized blockchain model to ensure security, privacy, service quality, and transparency. UAV virtualization allows persistent UAV services, globally, without procuring any physical UAVs. The platform offers virtual UAV services on a pay-per-use basis to the end-user. The UAV owners and virtual UAV service providers gain monetary benefits for their contribution to the UAV service offerings. Finally, we discuss the implications of the proposed platform on the domains of Industrial IoT and the possible challenges in the implementation of this paradigm.

A Robot Operating System Framework for Secure UAV Communications.

To perform advanced operations with unmanned aerial vehicles (UAVs), it is crucial that components other than the existing ones such as flight controller, network devices, and ground control station (GCS) are also used. The inevitable addition of hardware and software to accomplish UAV operations may lead to security vulnerabilities through various vectors. Hence, we propose a security framework in this study to improve the security of an unmanned aerial system (UAS). The proposed framework operates in the robot operating system (ROS) and is designed to focus on several perspectives, such as overhead arising from additional security elements and security issues essential for flight missions. The UAS is operated in a nonnative and native ROS environment. The performance of the proposed framework in both environments is verified through experiments.

Secure UAV Random Networks With Minimum Safety Distance

In this correspondence, we study the physical layer security in a stochastic unmanned aerial vehicles (UAVs) network from a network-wide perspective, where the locations of UAVs are modeled as a Mat$\acute{\text{e}}$rn hard-core point process (MHCPP) to characterize the minimum safety distance between UAVs, and the locations of users and eavesdroppers are modeled as a Poisson cluster process and a Poisson point process, respectively. UAVs adopt zero-forcing precoding to serve multiple ground users and emit artificial noise to combat eavesdropping. We derive the approximations for the coverage probability and secrecy probability of a typical user, with which we derive the secrecy throughput of the whole network. Numerical results show the analytical results can well approximate the simulation results. Impacts of parameters on the secrecy performance are shown.

Joint trajectory and power optimization for securing UAV communications against active eavesdropping

Due to both of jamming and eavesdropping, active eavesdroppers can induce more serious security threats to unmanned aerial vehicle (UAV)-enabled communications. This paper considers a secure UAV communication system including both the downlink (DL) and uplink (UL) transmissions, where the confidential information is transmitted between a UAV and a ground node in the presence of an active eavesdropper. We aim to maximize the average secrecy rates of the DL and UL communications, respectively, by jointly optimizing the UAV trajectory and the UAV/ground node's transmit power control over a given flight period. Due to the non-convexity of the formulated problems, it is difficult to obtain globally optimal solutions. However, we propose efficient iterative algorithms to obtain high-quality suboptimal solutions by applying the block coordinate descent and successive convex optimization methods. Simulation results show that the joint optimization algorithms can effectively improve the secrecy rate performance for both the DL and UL communications, as compared with other baseline schemes. The proposed schemes can be considered as special cases of UAV-assisted non-orthogonal multiple access (NOMA) networks.

Hierarchical Q-Learning Based UAV Secure Communication against Multiple UAV Adaptive Eavesdroppers

In this paper, we investigate secure unmanned aerial vehicle (UAV) communication in the presence of multiple UAV adaptive eavesdroppers (AEs), where each AE can conduct eavesdropping or jamming adaptively by learning others’ actions for degrading the secrecy rate more seriously. The one-leader and multi-follower Stackelberg game is adopted to analyze the mutual interference among multiple AEs, and the optimal transmit powers are proven to exist under the existing conditions. Following that, a mixed-strategy Stackelberg Equilibrium based on finite and discretized power set is also derived and a hierarchical Q-learning based power allocation algorithm (HQLA) is proposed to obtain the optimal power allocation strategy of the transmitter. Numerical results show that secrecy performance can be degraded severely by multiple AEs and verify the availability of the optimal power allocation strategy. Finally, the effect of the eavesdropping cost on the AE’s attack mode strategies is also revealed.

A taxonomy of blockchain-enabled softwarization for secure UAV network

The recent advancements in unmanned aerial vehicles (UAVs) upsurges its usages in commercial and civilian applications such as surveillance, rescue, and crowdsensing. UAVs are vulnerable to being destroyed, lost, or stolen in case of security breaches of its network. The network management of UAVs is a crucial task due to its high mobility, which necessitates UAV network softwarization. Then, it becomes indispensable that allows the separation of control functions (i.e., control plane data) from hardware for smooth execution of complex operations. Further, UAV uses the Internet (an open channel) for communication in its complex system that raises a network security concern. The well-known softwarization techniques, i.e., software-defined networking (SDN) and network function virtualization (NFV) can be used to accomplish the secure network services on less expense. Conversely, these softwarization techniques may suffer from various threats like access control, user authentication, controller hijacking, and many more attack. The existing solutions are using a centralized system, which is having a single point of failure issue and vulnerable to security threats. Motivated from these facts, we present a comprehensive and systematic survey on the blockchain-based softwarization for a secure UAV network. Then, we propose a blockchain-enabled UAV softwarization architecture for secure communication and network management. It provides dynamic, flexible, and on-the-fly decision capabilities for communication services over the UAV network. Eventually, we analyzed the open research issues and challenges for future research directions in this emerging area.

Joint 3D Maneuver and Power Adaptation for Secure UAV Communication With CoMP Reception

This paper studies a secrecy unmanned aerial vehicle (UAV) communication system with coordinated multi-point (CoMP) reception, in which one UAV sends confidential messages to a set of cooperative ground receivers (GRs), in the presence of several suspicious eavesdroppers. In particular, we consider two types of eavesdroppers that are non-colluding and colluding, respectively. Under this setup, we exploit the UAV's maneuver in three dimensional (3D) space together with transmit power adaptation for optimizing the secrecy communication performance. First, we consider the quasi-stationary UAV scenario, in which the UAV is placed at a fixed but optimizable location during the communication period. In this scenario, we jointly optimize the UAV's 3D placement and transmit power control to maximize the secrecy rate. Under both non-colluding and colluding eavesdroppers, we obtain the optimal solutions to the joint 3D placement and transmit power control problems in well structures. Next, we consider the mobile UAV scenario, in which the UAV has a mission to fly from an initial location to a final location during the communication period. In this scenario, we jointly optimize the UAV's 3D trajectory and transmit power allocation to maximize the average secrecy rate during the whole communication period. To deal with the difficult joint 3D trajectory and transmit power allocation problems, we present alternating-optimization-based approaches to obtain high-quality solutions. Finally, we provide numerical results to validate the performance of our proposed designs. It is shown that due to the consideration of CoMP reception, our proposed design with 3D maneuver significantly outperforms the conventional design with two dimensional (2D) (horizontal) maneuver only, by exploiting the additional degrees of freedom in altitudes. It is also shown that the non-colluding and colluding eavesdroppers lead to distinct 3D UAV maneuver behaviors, e.g., under colluding eavesdroppers, the UAV should fly farther apart from them (than that under the non-colluding ones) for avoiding their collaborative interception.

Resource Allocation for Secure Multi-UAV Communication Systems With Multi-Eavesdropper

In this paper, we study the resource allocation and trajectory design for secure unmanned aerial vehicle (UAV)-enabled communication systems, where multiple multi-purpose UAV base stations are dispatched to provide secure communications to multiple legitimate ground users (GUs) in the existence of multiple eavesdroppers (Eves). Specifically, by leveraging orthogonal frequency division multiple access (OFDMA), active UAV base stations can communicate to their desired ground users via the assigned subcarriers while idle UAV base stations can serve as jammer simultaneously for communication security provisioning. To achieve fairness in secure communication, we maximize the average minimum secrecy rate per user by jointly optimizing the communication/jamming subcarrier allocation policy and the trajectory of UAVs, while taking into account the constraints on the minimum safety distance among multiple UAVs, the maximum cruising speed, the initial/final locations, and the existence of cylindrical no-fly zones (NFZs). The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationally-efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm can significantly improve the average minimum secrecy rate compared to various baseline schemes.

Joint Trajectory and Resource Allocation Design for Energy-Efficient Secure UAV Communication Systems

In this paper, we study the trajectory and resource allocation design for downlink energy-efficient secure unmanned aerial vehicle (UAV) communication systems, where an information UAV assisted by a multi-antenna jammer UAV serves multiple ground users in the existence of multiple ground eavesdroppers. The resource allocation strategy and the trajectory of the information UAV, and the jamming policy of the jammer UAV are jointly optimized for maximizing the system energy efficiency. The joint design is formulated as a non-convex optimization problem taking into account the quality of service (QoS) requirement, the security constraint, and the imperfect channel state information (CSI) of the eavesdroppers. The formulated problem is generally intractable. As a compromise approach, the problem is divided into two subproblems which facilitates the design of a low-complexity suboptimal algorithm based on alternating optimization approach. Simulation results illustrate that the proposed algorithm converges within a small number of iterations and demonstrate some interesting insights: (1) the introduction of a jammer UAV facilitates a highly flexible trajectory design of the information UAV which is critical to improving the system energy efficiency; (2) by exploiting the spatial degrees of freedom brought by the multi-antenna jammer UAV, our proposed design can focus the artificial noise on eavesdroppers offering a strong security mean to the system.

Cooperative Jamming via Spectrum Sharing for Secure UAV Communications

This letter investigates the physical layer security issue in unmanned aerial vehicle (UAV) communication networks. Specifically, two UAVs transmit confidential messages to their specified ground destinations by sharing the same spectrum, while a ground eavesdropper attempts to wiretap the confidential messages. In the considered scenario, although mutual interferences arising from spectrum sharing may lower the spectral efficiency, the overhearing effect may also be alleviated as these two UAVs inherently act as cooperative jammers for each other. Motivated by this, a cooperation strategy is designed to combat eavesdropping, so that the system secrecy rate can be maximized. To this end, an iterative algorithm is proposed to jointly optimize the trajectory and transmit power of both UAVs. Simulations demonstrate that our proposed scheme can achieve significant secrecy rate gain in comparison with the benchmarks.

Secure Communications for UAV-Enabled Mobile Edge Computing Systems

In this paper, we propose a secure unmanned aerial vehicle (UAV) mobile edge computing (MEC) system where multiple ground users offload large computing tasks to a nearby legitimate UAV in the presence of multiple eavesdropping UAVs with imperfect locations. To enhance security, jamming signals are transmitted from both the full-duplex legitimate UAV and non-offloading ground users. For this system, we design a low-complexity iterative algorithm to maximize the minimum secrecy capacity subject to latency, minimum offloading and total power constraints. Specifically, we jointly optimize the UAV location, users’ transmit power, UAV jamming power, offloading ratio, UAV computing capacity, and offloading user association. Numerical results show that our proposed algorithm significantly outperforms baseline strategies over a wide range of UAV self-interference (SI) efficiencies, locations and packet sizes of ground users. Furthermore, we show that there exists a fundamental tradeoff between the security and latency of UAV-enabled MEC systems which depends on the UAV SI efficiency and total UAV power constraints.

Optimal power allocation for secure directional modulation networks with a full-duplex UAV user

This paper makes an investigation of a secure unmanned aerial vehicle (UAV)-aided communication network based on directional modulation (DM). In this network, ground base station (GBS) acts as a control center to transmit confidential message and artificial noise (AN). The UAV user, moving along a linear flight trajectory, is intended to receive the useful information from GBS. At the same time, it also sends AN signals to further interference eavesdroppers channel. Aiming at maximizing secrecy rate during the UAV flight process, a joint optimization problem is formulated with respect to power allocation (PA) factors, beamforming vector and AN projection matrices. For simplicity, maximum rate transmission, null-space projection and the leakage-based method are applied to form the transmit beamforming vector, AN projection matrix at GBS, and AN projection vector at UAV user, respectively. Following this, the optimization problem reduces to a bivariate optimization programme with two PA factors. An alternating iterative algorithm (AIA) is proposed to optimize the two PA factors. Simulation results demonstrate that, compared to the half-duplex (HD) mode, the proposed strategy for full-duplex (FD) mode achieves a higher secrecy rate (SR) and outperforms the FD mode with fixed PA strategy.

Cloud–SPHERE: Towards Secure UAV Service Provision

Unmanned Aerial Vehicles (UAVs) are gaining popularity in many fields and have now increased connectivity. Among others, security is considered one of the greatest challenges for UAV technology acceptance from the general public, more so when the UAV is connected to other vehicles and/or systems. In this paper, the first steps towards secure UAV service provision are presented through the Cloud–SPHERE platform, a security approach for integrating UAVs into the IoT ecosystem. UAV security and service provision are discussed and the prototype outlined. Preliminary service provision by UAVs using the MQTT protocol are presented.

Joint Trajectory and Communication Design for Secure UAV Networks

This letter investigates a joint optimization problem of unmanned aerial vehicle (UAV) flight trajectory, downlink transmission power, and ground terminals (GTs) association under wiretap channels. Specifically, we consider a scenario where a UAV serves a group of GTs and maximizes the minimum secrecy rate to ensure the fairness among GTs. To solve the nonconvex optimization problem, we develop an iterative algorithm based on the successive convex approximation and alternating methods. The simulation results demonstrate that the proposed algorithm is effective and can significantly improve the minimum secrecy rate compared with the traditional flight scheme.

Secure UAV Communication With Cooperative Jamming and Trajectory Control

This letter presents a new cooperative jamming approach to secure the unmanned aerial vehicle (UAV) communication by leveraging jamming from other nearby UAVs to defend against the eavesdropping. In particular, we consider a two-UAV scenario when one UAV transmitter delivers the confidential information to a ground node (GN), and the other UAV jammer cooperatively sends the artificial noise to confuse the ground eavesdropper for protecting the confidentiality of the data transmission. By exploiting the fully controllable mobility, the two UAVs can adaptively adjust their locations over time (a.k.a. trajectories) to facilitate the secure communication and cooperative jamming. We assume that the two UAVs perfectly know the GN's location and partially know the eavesdropper's location a priori. Under this setup, we maximize the average secrecy rate from the UAV transmitter to the GN over one particular time period, by optimizing the UAVs' trajectories, jointly with their communicating/jamming power allocations. Although the formulated problem is non-convex, we propose an efficient solution by applying the techniques of alternating optimization and successive convex approximation.