Field Testing and Detection of Camera Interference for Autonomous Driving

TL;DR

This paper presents a GRU-based intrusion detection system for detecting camera interference attacks in autonomous vehicles' Ethernet networks, demonstrating high accuracy through experimental validation.

Contribution

Introduces a novel GRU-based IDS with sliding-window preprocessing for effective detection of camera interference in automotive Ethernet environments.

Findings

Achieved an AUC of 0.9982 in detection accuracy.

True positive rate of 0.99 in experiments.

Effective differentiation of normal and anomalous data sequences.

Abstract

In recent advancements in connected and autonomous vehicles (CAVs), automotive ethernet has emerged as a critical technology for in-vehicle networks (IVNs), superseding traditional protocols like the CAN due to its superior bandwidth and data transmission capabilities. This study explores the detection of camera interference attacks (CIA) within an automotive ethernet-driven environment using a novel GRU-based IDS. Leveraging a sliding-window data preprocessing technique, our IDS effectively analyzes packet length sequences to differentiate between normal and anomalous data transmissions. Experimental evaluations conducted on a commercial car equipped with H.264 encoding and fragmentation unit-A (FU-A) demonstrated high detection accuracy, achieving an AUC of 0.9982 and a true positive rate of 0.99 with a window size of 255.