论文标题
诊断引导的攻击恢复，以防止机器人车免受传感器欺骗攻击
Diagnosis-guided Attack Recovery for Securing Robotic Vehicles from Sensor Deception Attacks
论文作者
论文摘要
传感器对于机器人车辆（RV）中的感知和自主操作至关重要。不幸的是，RV传感器可能会因诸如传感器篡改或欺骗等物理攻击而受到损害。在本文中，我们提出了DeLorean，这是一个统一的攻击检测，攻击诊断和从传感器欺骗攻击（SDA）中恢复RV的框架。 DeLorean甚至可以从强大的SDA中恢复RV，其中对手同时靶向多个异质传感器。我们提出了一种新型的攻击诊断技术，该技术检查了SDA下的攻击引起的错误，并使用因果分析确定了靶向传感器。然后，DeLorean使用历史态度信息来选择性地重建物理状态，以使其受损传感器，从而在单个或多传感器SDA下实现了目标攻击恢复。我们在针对各种传感器的SDA下对Delorean进行了四个真实和两个模拟的RV评估，我们发现它在93％的情况下成功地从SDA中恢复了RV。
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.
