无人机安全与深度强化学习调查

IF 4.4 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Burcu Sönmez Sarıkaya, Şerif Bahtiyar
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引用次数: 0

摘要

最近,由于无人驾驶飞行器(UAV)在不同环境中的适应性、自主性和灵活性,利用无人驾驶飞行器完成各种任务的做法受到了民用和军用组织的极大关注。无人机系统的特点带来了新的威胁,网络攻击可能从中受益。需要为无人机提供自适应安全解决方案,以应对日益增长的威胁。因此,无人机系统的安全性已成为发展最快的研究课题之一。基于机器学习的安全机制有可能提供有效的应对措施,对传统安全机制进行补充。这项调查的主要动机是缺乏有关基于强化学习的无人机系统安全解决方案的全面文献综述。在本文中,我们对无人机系统的安全性进行了全面综述,重点关注基于深度强化学习的安全解决方案。我们介绍了包括通信系统在内的无人机系统的一般架构,以显示潜在的漏洞来源。然后,探讨了无人机系统的威胁面。我们根据威胁系统地解释了对无人机系统的攻击。此外,我们还介绍了针对无人机的每种攻击的文献对策。此外,我们还解释了传统的防御机制,以强调无人机对基于强化的安全解决方案的需求。接下来,我们将介绍主要的强化算法。我们从整体上研究了强化学习算法的安全解决方案及其局限性。我们还确定了无人机上基于强化的安全解决方案所面临的研究挑战。简而言之,本调查报告提供了有关无人机系统、威胁、攻击、强化学习算法、无人机系统安全和研究挑战的关键指南。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A survey on security of UAV and deep reinforcement learning

Recently, the use of unmanned aerial vehicles (UAV)s for accomplishing various tasks has gained a significant interest from both civilian and military organizations due to their adaptive, autonomous, and flexibility nature in different environments. The characteristics of UAV systems introduce new threats from which cyber attacks may benefit. Adaptive security solutions for UAVs are required to counter the growing threat surface. The security of UAV systems has therefore become one of the fastest growing research topics. Machine learning based security mechanisms have a potential to provide effective countermeasures that complement traditional security mechanisms. The main motivation of this survey is to the lack of a comprehensive literature review about reinforcement learning based security solutions for UAV systems. In this paper, we present a comprehensive review on the security of UAV systems focusing on deep-reinforcement learning-based security solutions. We present a general architecture of an UAV system that includes communication systems to show potential sources of vulnerabilities. Then, the threat surface of UAV systems is explored. We explain attacks on UAV systems according to the threats in a systematic way. In addition, we present countermeasures in the literature for each attack on UAVs. Furthermore, traditional defense mechanisms are explained to highlight requirements for reinforcement based security solutions on UAVs. Next, we present the main reinforcement algorithms. We examine security solutions with reinforcement learning algorithms and their limitations in a holistic approach. We also identify research challenges about reinforcement based security solutions on UAVs. Briefly, this survey provides key guidelines on UAV systems, threats, attacks, reinforcement learning algorithms, the security of UAV systems, and research challenges.

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来源期刊
Ad Hoc Networks
Ad Hoc Networks 工程技术-电信学
CiteScore
10.20
自引率
4.20%
发文量
131
审稿时长
4.8 months
期刊介绍: The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.
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