RoboADS: Anomaly Detection Against Sensor and Actuator Misbehaviors in Mobile Robots

By Pinyao Guo, Hunmin Kim, Nurali Virani, Jun Xu, Minghui Zhu, Peng Liu. 2018

46 citations recorded by the source corpus.

Mobile robots such as unmanned vehicles integrate heterogeneous capabilities in sensing, computation, and control. They are representative cyber-physical systems where the cyberspace and the physical world are strongly coupled. However, the safety of mobile robots is significantly threatened by cyber/physical attacks and software/hardware failures. These threats can thwart normal robot operations and cause robot misbehaviors. In this paper, we propose a novel anomaly detection system, which leverages physical dynamics of mobile robots to detect misbehaviors in sensors and actuators. We explore issues raised in real-world implementations, e.g., distinctive robot dynamic models, sensor quantity and quality, decision parameters, etc., for practicality purposes. We implement the detection system on two types of mobile robots and evaluate the detection performance against various misbehavior scenarios, including signal interference, sensor spoofing, logic bomb and physical jamming. The experiments show detection effectiveness and small detection delays.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chaowei Xiao
2. Bo Li
3. Qi Alfred Chen
4. Yulong Cao
5. Chau Yuen
6. Jin Fang
7. Yuren Zhou
8. Dawei Yang

In Computer Networks and Communications

1. Chaowei Xiao
2. Billy Pik Lik Lau
3. Bo Li
4. Chau Yuen
5. Yuren Zhou
6. Meng Zhang
7. U-Xuan Tan
8. Ruigang Yang

In Control and Systems Engineering

1. Bo Li
2. Chau Yuen
3. Yuren Zhou
4. Meng Zhang
5. Billy Pik Lik Lau
6. U-Xuan Tan
7. Ruigang Yang
8. Marakkalage Sumudu Hasala

Citations in the corpus

• A survey of data fusion in smart city applications (2019, methodology): “Guo et al. [51] have proposed an anomaly detection to combat potential security aspects in the robots using sensor fusion technique such as state estimation.”
• Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks (2021, background): “This basic security design assumption is believed to hold in general [27], [28], and MSF is thus widely recognized as a general defense strategy against existing attacks on AD perception [10], [27]–[29].”