TrustConnect: An In-Vehicle Anomaly Detection Framework through Topology-Based Trust Rating

TL;DR

TrustConnect is a novel framework that assesses the trustworthiness of in-vehicle components by analyzing their interdependencies and vulnerabilities, aiming to detect and mitigate false information propagation in modern vehicle networks.

Contribution

It introduces a topology-based trust rating system for in-vehicle networks, integrating component interdependencies and exposure vulnerabilities for improved security assessment.

Findings

Effective in detecting untrustworthy components in simulated scenarios

Utilizes network topology and component vulnerabilities for trust evaluation

Validated through Python-based simulations across diverse network configurations

Abstract

Modern vehicles are equipped with numerous in-vehicle components that interact with the external environment through remote communications and services, such as Bluetooth and vehicle-to-infrastructure communication. These components form a network, exchanging information to ensure the proper functioning of the vehicle. However, the presence of false or fabricated information can disrupt the vehicle's performance. Given that these components are interconnected, erroneous data can propagate throughout the network, potentially affecting other components and leading to catastrophic consequences. To address this issue, we propose TrustConnect, a framework designed to assess the trustworthiness of a vehicle's in-vehicle network by evaluating the trust levels of individual components under various network configurations. The proposed framework leverages the interdependency of all the vehicle's components, along with the correlation of their values and their vulnerability to remote injection based on the outside exposure of each component, to determine the reliability of the in-vehicle network. The effectiveness of the proposed framework has been validated through programming simulations conducted across various scenarios using a random distribution of an in-vehicle network graph generated with the Networkx package in Python.