面向传感器欺骗攻击下机器人车辆安全的诊断引导攻击恢复
Diagnosis-guided Attack Recovery for Securing Robotic Vehicles from Sensor Deception Attacks
AI总结：
本文提出DeLorean统一框架，通过因果分析诊断传感器欺骗攻击并利用历史状态重建受损传感器物理状态，实现机器人车辆在多传感器攻击下的针对性恢复，成功率93%。
摘要：
传感器对于机器人车辆（RV）的感知和自主运行至关重要。不幸的是，RV传感器可能会受到物理攻击（如传感器篡改或欺骗）的危害。在本文中，我们提出了DeLorean，这是一个用于攻击检测、攻击诊断以及从传感器欺骗攻击（SDA）中恢复RV的统一框架。即使面对攻击者同时针对多个异构传感器的强SDA，DeLorean也能恢复RV。我们提出了一种新颖的攻击诊断技术，用于检查SDA下由攻击引起的错误，并利用因果分析识别被攻击的传感器。随后，DeLorean利用历史状态信息为受损传感器选择性地重建物理状态，从而实现单传感器或多传感器SDA下的针对性攻击恢复。我们在四辆真实和两辆模拟的RV上评估了DeLorean，这些RV面临针对各种传感器的SDA，我们发现它在93%的案例中成功从SDA中恢复了RV。
英文摘要：
Sensors are crucial for perception and autonomous operation in robotic vehicles (RV). Unfortunately, RV sensors can be compromised by physical attacks such as sensor tampering or spoofing. In this paper, we present DeLorean, a unified framework for attack detection, attack diagnosis, and recovering RVs from sensor deception attacks (SDA). DeLorean can recover RVs even from strong SDAs in which the adversary targets multiple heterogeneous sensors simultaneously. We propose a novel attack diagnosis technique that inspects the attack-induced errors under SDAs, and identifies the targeted sensors using causal analysis. DeLorean then uses historic state information to selectively reconstruct physical states for compromised sensors, enabling targeted attack recovery under single or multi-sensor SDAs. We evaluate DeLorean on four real and two simulated RVs under SDAs targeting various sensors, and we find that it successfully recovers RVs from SDAs in 93% of the cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
