[论文解读] Unveiling the Stealthy Threat: Analyzing Slow Drift GPS Spoofing Attacks for Autonomous Vehicles in Urban Environments and Enabling the Resilience

Sagar Dasgupta, Abdullah Akhtar Ahmed+2 more|arXiv (Cornell University)|Jan 2, 2024

Forensic Toxicology and Drug Analysis被引用 4

一句话总结

本文提出了一种针对城市环境中自动驾驶汽车的隐蔽性缓慢漂移GPS欺骗攻击，通过模仿目标车辆的卫星接收模式，同时逐渐改变伪距，以诱导路径偏离。由于原始伪距与欺骗伪距之间具有极强的相关性（R² = 0.99–1.0），该攻击保持隐蔽，可通过信号相关性分析实现有效检测与缓解。

ABSTRACT

Autonomous vehicles (AVs) rely on the Global Positioning System (GPS) or Global Navigation Satellite Systems (GNSS) for precise (Positioning, Navigation, and Timing) PNT solutions. However, the vulnerability of GPS signals to intentional and unintended threats due to their lack of encryption and weak signal strength poses serious risks, thereby reducing the reliability of AVs. GPS spoofing is a complex and damaging attack that deceives AVs by altering GPS receivers to calculate false position and tracking information leading to misdirection. This study explores a stealthy slow drift GPS spoofing attack, replicating the victim AV's satellite reception pattern while changing pseudo ranges to deceive the AV, particularly during turns. The attack is designed to gradually deviate from the correct route, making real-time detection challenging and jeopardizing user safety. We present a system and study methodology for constructing covert spoofing attacks on AVs, investigating the correlation between original and spoofed pseudo ranges to create effective defenses. By closely following the victim vehicle and using the same satellite signals, the attacker executes the attack precisely. Changing the pseudo ranges confuses the AV, leading it to incorrect destinations while remaining oblivious to the manipulation. The gradual deviation from the actual route further conceals the attack, hindering its swift identification. The experiments showcase a robust correlation between the original and spoofed pseudo ranges, with R square values varying between 0.99 and 1. This strong correlation facilitates effective evaluation and mitigation of spoofing signals.

研究动机与目标

• 研究在城市环境中缓慢漂移GPS欺骗攻击的可行性和隐蔽性。
• 理解渐进式伪距操纵如何在不立即触发警报的情况下欺骗自动驾驶汽车。
• 通过复制目标车辆的卫星接收模式，构建隐蔽欺骗攻击的系统。
• 通过分析原始与欺骗伪距之间的相关性，实现具有韧性的防御机制。
• 通过实测信号相关性分析评估此类攻击的可检测性。

提出的方法

• 攻击通过近距离欺骗装置复制目标车辆的卫星信号接收模式。
• 逐步改变伪距，以在自动驾驶汽车的导航系统中引发错误的位置和轨迹计算。
• 对原始与欺骗伪距之间进行基于相关性的分析，以检测异常。
• 利用实时信号监控与同步技术，在城市驾驶场景中保持隐蔽性。
• 在受控环境中模拟城市条件，以验证欺骗的有效性。
• 该方法可通过识别合法信号与欺骗信号之间相关性模式的偏差，实现检测。

实验结果

研究问题

• RQ1 如何设计一种缓慢漂移GPS欺骗攻击，使其在自动驾驶汽车城市运行过程中保持隐蔽？
• RQ2 此类攻击中，原始与欺骗伪距之间的相关程度如何？
• RQ3 渐进式伪距操纵如何在不立即触发检测的情况下影响自动驾驶汽车的导航？
• RQ4 能否利用原始与欺骗信号之间的相关性来检测并缓解欺骗攻击？
• RQ5 此类隐蔽欺骗在真实城市环境中的实际限制和可检测性阈值是什么？

主要发现

• 欺骗攻击在原始与欺骗伪距之间实现了极高的相关性（R² = 0.99–1.0），表明信号保真度极高。
• 伪距的渐进性偏差使攻击在实时过程中保持隐蔽，增强了在城市环境中的隐蔽性。
• 信号之间的相关性模式可有效用于检测欺骗，通过识别不自然的信号漂移。
• 该攻击成功使自动驾驶汽车的导航系统偏离正确路线，而未立即触发系统警报。
• 实验设置证实，欺骗信号与合法信号高度相似，验证了攻击的可行性和隐蔽性。
• 结果表明，信号相关性分析是检测细微、缓慢漂移欺骗攻击的可行方法。