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

• URL: http://arxiv.org/abs/2506.06635v1
• Date: Sat, 07 Jun 2025 03:06:41 GMT
• Title: TrustConnect: An In-Vehicle Anomaly Detection Framework through Topology-Based Trust Rating
• Authors: Ayan Roy, Jeetkumar Patel, Rik Chakraborti, Shudip Datta,
• Abstract summary: We propose TrustConnect, a framework designed to assess the trustworthiness of a vehicle's in-vehicle network.<n>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.<n>The effectiveness of the proposed framework has been validated through programming simulations conducted across various scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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