The Small and Silent Force Multiplier: A Swarm UAV—Electronic Attack

Cevik, Polat; Kocaman, Ibrahim; Akgül, A.Serkan; Akca, Barbaros

doi:10.1007/s10846-012-9698-1

Cited by 32 publications

(22 citation statements)

References 9 publications

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“…UAV swarms are generally considered to be self‐organizing robotic systems consisting of small UAVs with the same or similar targets flying in the same flight mode 9,10 . The organization and management of multidimensional resources of UAV swarm is the basis for completing missions cooperatively, and the reliable and efficient UAV swarm networks (USNET) is the prerequisite for resource status perception and management.…”

Section: Related Workmentioning

confidence: 99%

A distributed fault‐tolerant mechanism for mission‐oriented unmanned aerial vehicle swarms

Wang

Chen

2021

Int J Communication

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Summary Unmanned aerial vehicle (UAV) swarm consists of a number of autonomous and collaborative UAV nodes. The purpose of the research is to address the hypotheses: an efficient centralized resources management scheme should be adopted to meet the mission‐oriented feature of UAV swarms, while a distributed fault‐tolerant mechanism should be adopted to meet the characteristics of complex environment and address the issue of high failure rate of UAV nodes. By using an experimental modeling method, this paper proposes a management model and related mechanisms fitting for UAV swarms with the feature of “centralized decision making and distributed task execution.” The model allows UAV nodes to manage their own resources autonomously and execute centralized decisions accordingly. The advantage of the model is that the master node of a UAV swarm is able to perceive all the resources of the system and the status of all (or some) of the UAVs efficiently; therefore, it provides a supportive environment for the UAV swarm application to make centralized decisions and optimize those decisions. Additionally, this paper proposes a distributed fault‐tolerant model and related mechanisms to address the issue of high failure rate of UAV nodes, as well as the system vulnerability caused by harsh environment. Finally, this study developed a prototype system designed and implemented by OMNeT++ to confirm the truth of the hypotheses. The analysis of the experimental data and simulation results of the prototype validated the feasibility and usability of the distributed fault‐tolerant mechanism and demonstrated its value for the mission‐oriented UAV swarms.

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Section: Related Workmentioning

confidence: 99%

A distributed fault‐tolerant mechanism for mission‐oriented unmanned aerial vehicle swarms

Wang

Chen

2021

Int J Communication

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“…Rahmes Mark et al designed a multi-UAV countermeasure system and used Q-Learning algorithm to generate radar transmitting frequency [18]. Cevik Polat et al reduced the resource allocation problem in electronic countermeasures to an optimal allocation problem, and proposed a resource allocation algorithm based on particle swarm optimization theory [19].…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems

Wang

et al. 2019

Applied Sciences

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The networked unmanned aerial vehicle (UAV) radar system may exploit inter-UAV cooperation for enhancing information acquisition capabilities, meanwhile its inter-UAV communications may be interfered with by external jammers. This paper is devoted to quantifying and optimizing the anti-jamming performance of networked UAV radar systems in adversarial electromagnetic environments. Firstly, instead of using the conventional metric of signal-to-interference ratio (SIR), this paper explores use of the theory of radar information representation as the basis of evaluating the information acquisition capabilities of the networked UAV radar systems. Secondly, this paper proposes a modified Q-Learning method based on double greedy algorithm to optimize the anti-jamming performance of the networked UAV radar systems, through joint programming in the frequency-motion-antenna domain. Simulation results prove the effectiveness of the algorithm in two different networking scenarios.

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“…The jamming attack has been considered in previous research on various wireless networks [9]- [23]. In recent years, various solutions have also been proposed for the jamming attack in the UAVs [9], [13], [15]- [17], [20]. However, the proposed approaches of jamming attack detection for the UAVs are not well suited for FANET as jamming attack detection in the FANET faces three major challenges:…”

Section: Introductionmentioning

confidence: 99%

Federated Learning-Based Cognitive Detection of Jamming Attack in Flying Ad-Hoc Network

et al. 2020

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Flying Ad-hoc Network (FANET) is a decentralized communication system solely formed by Unmanned Aerial Vehicles (UAVs). In FANET, the UAV clients are vulnerable to various malicious attacks such as the jamming attack. The aerial adversaries in the jamming attack disrupt the communication of the victim network through interference on the receiver side. Jamming attack detection in FANET poses new challenges for its key differences from other ad-hoc networks. First, because of the varying communication range and power consumption constraints, any centralized detection system becomes trivial in FANET. Second, the existing decentralized solutions, disregarding the unbalanced sensory data from new spatial environments, are unsuitable for the highly mobile and spatially heterogeneous UAVs in FANET. Third, given a huge number of UAV clients, the global model may need to choose a sub-group of UAV clients for providing a timely global update. Recently, federated learning has gained attention, as it addresses unbalanced data properties besides providing communication efficiency, thus making it a suitable choice for FANET. Therefore, we propose a federated learning-based on-device jamming attack detection security architecture for FANET. We enhance the proposed federated learning model with a client group prioritization technique leveraging the Dempster-Shafer theory. The proposed client group prioritization mechanism allows the aggregator node to identify better client groups for calculating the global update. We evaluated our mechanism with datasets from publicly available standardized jamming attack scenarios by CRAWDAD and the ns-3 simulated FANET architecture and showed that, in terms of accuracy, our proposed solution (82.01% for the CRAWDAD dataset and 89.73% for the ns-3 simulated FANET dataset) outperforms the traditional distributed solution (49.11% for the CRAWDAD dataset and 65.62% for the ns-3 simulated FANET dataset). Moreover, the Dempster-Shafer-based client group prioritization mechanism identifies the best client groups out of 56 client group combinations for efficient federated averaging.INDEX TERMS Unmannad aerial vehicle, flying ad-hoc network, jamming attack, federated learning, ondevice AI, Dempster-Shafer theory.

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The Small and Silent Force Multiplier: A Swarm UAV—Electronic Attack

Cited by 32 publications

References 9 publications

A distributed fault‐tolerant mechanism for mission‐oriented unmanned aerial vehicle swarms

A distributed fault‐tolerant mechanism for mission‐oriented unmanned aerial vehicle swarms

Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems

Federated Learning-Based Cognitive Detection of Jamming Attack in Flying Ad-Hoc Network

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