Secure UAV Communication Networks via Friendly Jamming and Bandwidth Allocation

Zhang, Huilian; He, Xiaofan; Dai, Huaiyu

doi:10.1109/infocomwkshps50562.2020.9162671

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…based on the monotonicity of the log function. For the nonconcavity of the left side term of the constraint (27), we utilize the first order Taylor expansion to get the lower bound of the term 1…”

Section: A Transmit Power Allocationmentioning

confidence: 99%

“…In references [25], [26], the authors proposed an artificial noise (AN) method and the means of joint trajectory and power optimization to resist eavesdropping and improve system secrecy rate, respectively. In [27], [28], the authors maximize secrecy rate of the UAV relay with a UAV jammer and an active eavesdropper whose location is not fixed. An effective method was proposed to achieve the best security performance of the system with multiple randomly distributed eavesdroppers in [29].…”

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confidence: 99%

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

et al. 2022

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Section: A Transmit Power Allocationmentioning

confidence: 99%

mentioning

confidence: 99%

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

et al. 2022

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“…UAV jammer has also been integrated into UAV relaying systems [180], [182], [188], [191]. In [191], for such dual-UAV system with one UAV relay and one UAV jammer, the authors proposed an iterative algorithm via optimizing the trajectories and transmit power for both UAVs to maximize the SR.…”

Section: Trajectory Optimizationmentioning

confidence: 99%

“…In [180], the constraints of flight trajectory and power on both UAVs, as well as the information-causality constraint on the relaying UAV, were jointly considered in the optimization to maximize the SR. The paper [182] considered a multi-UAV communication network, where a novel joint friendly jamming and bandwidth allocation scheme was developed.…”

Section: Trajectory Optimizationmentioning

confidence: 99%

Physical Layer Security for UAV Communications in 5G and Beyond Networks

Wang,

Gao

et al. 2021

Preprint

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Due to its high mobility and flexible deployment, unmanned aerial vehicle (UAV) is drawing unprecedented interest in both military and civil applications to enable agile wireless communications and provide ubiquitous connectivity. Mainly operating in an open environment, UAV communications can benefit from dominant line-of-sight links; however, it on the other hand renders the UAVs more vulnerable to malicious eavesdropping or jamming attacks. Recently, physical layer security (PLS), which exploits the inherent randomness of the wireless channels for secure communications, has been introduced to UAV systems as an important complement to the conventional cryptographybased approaches. In this paper, a comprehensive survey on the current achievements of the UAV-aided wireless communications is conducted from the PLS perspective. We first introduce the basic concepts of UAV communications including the typical static/mobile deployment scenarios, the unique characteristics of air-to-ground channels, as well as various roles that a UAV may act when PLS is concerned. Then, we introduce the widely used secrecy performance metrics and start by reviewing the secrecy performance analysis and enhancing techniques for statically deployed UAV systems, and extend the discussion to a more general scenario where the UAVs' mobility is further exploited. For both cases, respectively, we summarize the commonly adopted methodologies in the corresponding analysis and design, then describe important works in the literature in detail. Finally, potential research directions and challenges are discussed to provide an outlook for future works in the area of UAV-PLS in 5G and beyond networks.

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“…DR: The number of malicious UAVs detected by smart agents in the proposed method is divided by the total number of malicious UAVs in the UAV network [28,29]. The DR is shown in Eq.…”

Section: Performance Metricsmentioning

confidence: 99%

Secure communication between UAVs using a method based on smart agents in unmanned aerial vehicles

Faraji-Biregani¹,

Fotohi

2020

J Supercomput

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Unmanned Aerial Vehicles (UAVs) can be deployed to monitor very large areas without the need for network infrastructure. UAVs communicate with each other during flight and exchange information with each other. However, such communication poses security challenges due to its dynamic topology. To solve these challenges, the proposed method uses two phases to counter malicious UAV attacks. In the first phase, we applied a number of rules and principles to detect malicious UAVs. In this phase, we try to identify and remove malicious UAVs according to the behavior of UAVs in the network in order to prevent sending fake information to the investigating UAVs. In the second phase, a mobile agent based on a three-step negotiation process is used to eliminate malicious UAVs. In this way, we use mobile agents to inform our normal neighbor UAVs so that they do not listen to the data generated by the malicious UAVs. Therefore, the mobile agent of each UAV uses reliable neighbors through a three-step negotiation process so that they do not listen to the traffic generated by the malicious UAVs. The NS-3 simulator was used to demonstrate the efficiency of the SAUAV method. The proposed method is more efficient than CST-UAS, CS-AVN, HVCR, and BSUM-based methods in detection rate, false positive rate, false negative rate, packet delivery rate, and residual energy.

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Secure UAV Communication Networks via Friendly Jamming and Bandwidth Allocation

Cited by 11 publications

References 31 publications

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

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

Physical Layer Security for UAV Communications in 5G and Beyond Networks

Secure communication between UAVs using a method based on smart agents in unmanned aerial vehicles

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