无人机群网络安全调查:攻击与对策

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-11-08 DOI:10.1145/3703625
Xiaojie Wang, Zhonghui Zhao, Ling Yi, Zhaolong Ning, Lei Guo, F. Richard Yu, Song Guo
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引用次数: 0

摘要

无人机群之所以越来越受欢迎,是因为它们能够以较低的成本为各行各业带来可观的回报。此外,在未来的无线网络格局中,无人机群可以充当空中基站,缓解通信资源稀缺的问题。然而,无人机群网络容易受到各种安全威胁,攻击者可以利用这些威胁造成不可预知的后果。在此背景下,本文对无人机蜂群网络的安全性进行了全面评述。我们首先简要介绍了主流的无人机蜂群技术,然后介绍了其民用和军用应用。然后,我们对无人机蜂群网络可能遇到的各种潜在攻击进行了介绍和分类,如拒绝服务攻击、中间人攻击和针对机器学习(ML)模型的攻击。随后,我们介绍了可用于应对这些攻击的安全技术,包括密码学、物理层安全技术、区块链、ML 和入侵检测。此外,我们还研究并总结了应对无人机蜂群网络中不同安全威胁的缓解策略。最后,讨论了一些研究方向和挑战。
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A Survey on Security of UAV Swarm Networks: Attacks and Countermeasures
The increasing popularity of Unmanned Aerial Vehicle (UAV) swarms is attributed to their ability to generate substantial returns for various industries at a low cost. Additionally, in the future landscape of wireless networks, UAV swarms can serve as airborne base stations, alleviating the scarcity of communication resources. However, UAV swarm networks are vulnerable to various security threats that attackers can exploit with unpredictable consequences. Against this background, this paper provides a comprehensive review on security of UAV swarm networks. We begin by briefly introducing the dominant UAV swarm technologies, followed by their civilian and military applications. We then present and categorize various potential attacks that UAV swarm networks may encounter, such as denial-of-service attacks, man-in-the-middle attacks and attacks against Machine Learning (ML) models. After that, we introduce security technologies that can be utilized to address these attacks, including cryptography, physical layer security techniques, blockchain, ML, and intrusion detection. Additionally, we investigate and summarize mitigation strategies addressing different security threats in UAV swarm networks. Finally, some research directions and challenges are discussed.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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