Strategic Defense Against Deceptive Civilian GPS Spoofing of Unmanned Aerial Vehicles
Strategic Defense Against Deceptive Civilian GPS Spoofing of Unmanned Aerial Vehicles
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DOI：
10.1007/978-3-319-68711-7_12
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Tao Zhang;Quanyan Zhu
中科院分区：
文献类型：
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作者：
Tao Zhang;Quanyan Zhu
全球定位系统（GPS）通常用于民用无人机（uav），为导航提供地理位置和时间信息。然而，GPS容易受到许多故意的威胁，例如GPS信号欺骗，攻击者可以通过广播错误的GPS信号来欺骗GPS接收器。防御此类攻击对于确保无人机的可靠性和安全性至关重要。在这项工作中，我们提出了一个信号博弈框架，在该框架中，当攻击者试图用欺诈性和有目的地制作的信号误导GPS接收器时，GPS接收器可以战略性地推断出真实位置。我们刻画了该博弈的完美贝叶斯均衡（PBE）的充分必要条件，观察到该均衡具有PLASH结构，即低类型池化，高类型分离。这种结构能够建立一种博弈论的安全机制来防御民用无人机的民用GPS信号欺骗。研究结果表明，在PLASH PBE的分离部分，民用无人机可以推断出自己在欺骗攻击下的真实位置，而在PLASH PBE的池化部分，相应的平衡策略使民用无人机能够合理地选择与真实位置偏差最小的位置。数值实验证实了我们的结果和观察结果。
The Global Positioning System (GPS) is commonly used in civilian Unmanned Aerial Vehicles (UAVs) to provide geolocation and time information for navigation. However, GPS is vulnerable to many intentional threats such as the GPS signal spoofing, where an attacker can deceive a GPS receiver by broadcasting incorrect GPS signals. Defense against such attacks is critical to ensure the reliability and security of UAVs. In this work, we propose a signaling game framework in which the GPS receiver can strategically infer the true location when the attacker attempts to mislead it with a fraudulent and purposefully crafted signal. We characterize the necessary and sufficient conditions of perfect Bayesian equilibrium (PBE) of the game and observe that the equilibrium has a PLASH structure, i.e., pooling in low types and separating in high types. This structure enables the development of a game-theoretic security mechanism to defend against the civil GPS signal spoofing for civilian UAVs. Our results show that in the separating part of the PLASH PBE, the civilian UAV can infer its true position under the spoofing attack while in the pooling portion of the PLASH PBE, the corresponding equilibrium strategy allows the civilian UAV to rationally decide the position that minimizes the deviation from its true position. Numerical experiments are used to corroborate our results and observations.