Improvement and Evaluation of Resilience of Adaptive Cruise Control Against Spoofing Attacks Using Intrusion Detection System

TL;DR

This paper enhances adaptive cruise control by integrating a real-time machine learning-based intrusion detection system to identify and mitigate speed spoofing attacks, thereby improving vehicle safety against cyber threats.

Contribution

The paper introduces a novel ML-based intrusion detection system for ACC that detects spoofing attacks in real-time and triggers safety responses, improving resilience against cyber-attacks.

Findings

Spoofing can cause false acceleration and accidents.

ML-based IDS can detect spoofing with reasonable response time.

Triggering brakes upon attack detection mitigates crash risk.

Abstract

The Adaptive Cruise Control (ACC) system automatically adjusts the vehicle speed to maintain a safe distance between the vehicle and the lead (ahead) vehicle. The controller's decision to accelerate or decelerate is computed using the target speed of the vehicle and the difference between the vehicle's distance to the lead vehicle and the safe distance from that vehicle. Spoofing the vehicle speed communicated through the Controller Area Network (CAN) of the vehicle impacts negatively the capability of the ACC (Proportional-Integral-Derivative variant) to prevent crashes with the lead vehicle. The paper reports about extending the ACC with a real-time Intrusion Detection System (IDS) capable of detecting speed spoofing attacks with reasonable response time and detection rate, and simulating the proposed extension using the CARLA simulation platform. The results of the simulation are: (1) spoofing the vehicle speed can foil the ACC to falsely accelerate, causing accidents, and (2) extending ACC with ML-based IDS to trigger the brakes when an accident is imminent may mitigate the problem. The findings suggest exploring the capabilities of ML-based IDS to support the resilience mechanisms in mitigating cyber-attacks on vehicles.