An Anomaly Behavior Analysis Framework for Securing Autonomous Vehicle Perception

TL;DR

This paper introduces a novel anomaly detection framework for autonomous vehicle perception sensors, combining physics-based models and machine learning to identify sophisticated sensor attacks, validated through real-world experiments and a public dataset.

Contribution

It presents a new anomaly detection framework that integrates physics-based behavior models with machine learning, and provides the first public dataset of perception sensor attacks on autonomous vehicles.

Findings

Effective detection of perception sensor attacks demonstrated

Framework validated with real-world attack data

Public dataset released for research use

Abstract

As a rapidly growing cyber-physical platform, Autonomous Vehicles (AVs) are encountering more security challenges as their capabilities continue to expand. In recent years, adversaries are actively targeting the perception sensors of autonomous vehicles with sophisticated attacks that are not easily detected by the vehicles' control systems. This work proposes an Anomaly Behavior Analysis approach to detect a perception sensor attack against an autonomous vehicle. The framework relies on temporal features extracted from a physics-based autonomous vehicle behavior model to capture the normal behavior of vehicular perception in autonomous driving. By employing a combination of model-based techniques and machine learning algorithms, the proposed framework distinguishes between normal and abnormal vehicular perception behavior. To demonstrate the application of the framework in practice, we performed a depth camera attack experiment on an autonomous vehicle testbed and generated an extensive dataset. We validated the effectiveness of the proposed framework using this real-world data and released the dataset for public access. To our knowledge, this dataset is the first of its kind and will serve as a valuable resource for the research community in evaluating their intrusion detection techniques effectively.