A Physics-Informed Context-Aware Approach for Anomaly Detection in Tele-driving Operations Under False Data Injection Attacks

TL;DR

This paper presents a physics-informed, context-aware anomaly detection system for tele-driving systems to identify false data injection attacks, enhancing cyber-physical security in autonomous vehicle operations.

Contribution

It introduces a novel attack model based on real vehicle data and develops a PCADS to detect false data injection attacks in tele-operated driving systems.

Findings

The attack model effectively simulates FDI attacks on steering commands.

Preliminary experiments validate the PCADS's ability to detect false data injections.

The approach enhances security by integrating physics-based and contextual information.

Abstract

Tele-operated driving (ToD) systems are special types of cyber-physical systems (CPSs) where the operator remotely controls the steering, acceleration, and braking actions of the vehicle. Malicious actors may inject false data in communication channels to manipulate the tele-operators driving commands to cause harm. Hence, protection of this communication is necessary for the safe operation of the target vehicle. However, according to the National Institute of Standards and Technology (NIST) cybersecurity framework, protection merely is not enough and the detection of an attack is necessary. Moreover, UN R155 mandates that security incidents across vehicle fleets be detected and logged. Thus, cyber-physical threats of ToD are modeled with an attack-centric approach in this paper. Then, an attack model with false data injection (FDI) on steering control commands is created from real vehicle data. The risk of this attack model is assessed for a last-mile delivery (LMD) application. Finally, a physics-informed context-aware anomaly detection system (PCADS) is proposed to detect such false injection attacks, and preliminary experimental results are presented to validate the model.