$AutoGuardX$: A Comprehensive Cybersecurity Framework for Connected Vehicles

TL;DR

AutoGuardX is a comprehensive cybersecurity framework for connected vehicles that integrates standards, machine learning, and advanced protocols to defend against modern cyber threats, validated through extensive simulations.

Contribution

It introduces AutoGuardX, a novel security framework combining recognized standards with advanced technologies tailored for connected vehicle cybersecurity.

Findings

Effective detection of cyber threats using machine learning

Demonstrated scalability across different vehicle models

Robust protection against relay and CAN bus attacks

Abstract

The rapid integration of Internet of Things (IoT) and interconnected systems in modern vehicles not only introduced a new era of convenience, automation, and connected vehicles but also elevated their exposure to sophisticated cyber threats. This is especially evident in US and Canada, where cyber-enabled auto theft has surged in recent years, revealing the limitations of existing security measures for connected vehicles. In response, this paper proposes $AutoGuardX$, a comprehensive cybersecurity framework designed specifically for connected vehicles. $AutoGuardX$ combines key elements from existing recognized standards for vehicle security, such as ISO/SAE 21434 and ISO 26262, with advanced technologies, including machine learning-based anomaly detection, IoT security protocols, and encrypted communication channels. The framework addresses major attack vectors like relay attacks, controller area network (CAN) bus intrusions, and vulnerabilities introduced by emerging technologies such as 5G and quantum computing. $AutoGuardX$ is extensively evaluated through security simulations across a mix of Sedans and SUVs from four major vehicle brands manufactured between 2019 and 2023. The results demonstrate the framework's adaptability, scalability, and practical effectiveness against existing and emerging threats.