# Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles

arXiv: https://arxiv.org/abs/2212.07757 · HTML: https://mlsanity.com/paper/2212.07757/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving](https://mlsanity.com/paper/1907.06826/) (2019)
- [Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures](https://mlsanity.com/paper/2006.16974/) (2020)
- [Security Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehicles](https://mlsanity.com/paper/2106.07098/) (2021)
- [Fooling LiDAR Perception via Adversarial Trajectory Perturbation](https://mlsanity.com/paper/2103.15326/) (2021)
- [Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash](https://mlsanity.com/paper/1805.11815/) (2018)
- [A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2007.08041/) (2020)
- [Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach](https://mlsanity.com/paper/2204.12970/) (2022)
- [Topology Learning Aided False Data Injection Attack without Prior Topology Information](https://mlsanity.com/paper/2102.12248/) (2021)

## Abstract (arXiv)

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 textitSTAnDS 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 textitSTAnDS is effective at detecting multiple attack types.