Work

CANini: In-Depth Traffic Analysis for Design and Robustness Evaluation of DTree-Based IDS in Automotive Networking Systems

Year: 2025

Type: article

Abstract: In-Vehicle Networks (IVNs) have become increasingly complex due to the need to enhance driving experience and provide passengers with a broader range of services. The Controller Area Network (CAN) bus is widely used in modern vehicles, enabling communication between various electronic control units. However, its cybersecurity vulnerabilities require the use of protection mechanisms, such as an Intrusion Detection System (IDS) that can efficiently identify and mitigate threats in CAN networks. In this paper, we perform an extensive and thorough analysis of a CAN dataset, namely CANini. The dataset comes from the augmentation of the CAN-MIRGU dataset, which has been collected from a real vehicle under various driving scenarios. Our analysis offers a comprehensive and extensive characterization of CAN network traffic, examining specifically inter-frame arrival times and patterns in data bytes. We proceeded to test these features by designing a simple anomaly-based IDS that employs a multiclass Decision Tree classifier. Then, a robustness analysis on the IDS has been performed by manipulating the properties of the CAN traffic to find the weaknesses of our selected architecture. Our novel analysis provides critical insights into the behavior and structure of CAN in a typical IVN, which is crucial for the development and validation of a reliable IDS.

Source: IEEE Access

DOI: 10.1109/access.2025.3561591

Authors: Nicasio Canino, Pierpaolo Dini, Stefano Mazzetti, Daniele Rossi, Sergio Saponara

Institution: University of Pisa

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,075