论文标题
自动驾驶汽车传感器攻击的时空异常检测
Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles
论文作者
论文摘要
飞行时间（TOF）距离测量设备（例如超声波，激光雷达和雷达）被广泛用于自动驾驶汽车中，用于环境感知，导航和辅助制动控制。尽管它们在做出更安全的驾驶决策方面相对重要，但这些设备容易受到多种攻击类型的影响，包括欺骗，触发和错误的数据注入。当这些攻击成功时，它们可能会损害自动驾驶汽车的安全性，从而对驾驶员，附近的车辆和行人造成严重后果。为了处理这些攻击并保护测量设备，我们提出了一个时空异常检测模型\ textIt {stand}，该模型{stand}结合了残余误差空间检测器，并带有基于时间的预期变化检测。使用模拟的定量环境评估了此方法，结果表明\ textit {stand}有效地检测多种攻击类型。
Time-of-flight (ToF) distance measurement devices such as ultrasonics, LiDAR and radar are widely used in autonomous vehicles for environmental perception, navigation and assisted braking control. Despite their relative importance in making safer driving decisions, these devices are vulnerable to multiple attack types including spoofing, triggering and false data injection. When these attacks are successful they can compromise the security of autonomous vehicles leading to severe consequences for the driver, nearby vehicles and pedestrians. To handle these attacks and protect the measurement devices, we propose a spatial-temporal anomaly detection model \textit{STAnDS} which incorporates a residual error spatial detector, with a time-based expected change detection. This approach is evaluated using a simulated quantitative environment and the results show that \textit{STAnDS} is effective at detecting multiple attack types.
