A Critical Roadmap to Driver Authentication via CAN Bus: Dataset Review, Introduction of the Kidmose CANid Dataset (KCID), and Proof of Concept
- URL: http://arxiv.org/abs/2510.25856v2
- Date: Sat, 01 Nov 2025 12:36:18 GMT
- Title: A Critical Roadmap to Driver Authentication via CAN Bus: Dataset Review, Introduction of the Kidmose CANid Dataset (KCID), and Proof of Concept
- Abstract summary: Criminals exploit vulnerabilities in Controller Area Network (CAN) bus systems to bypass authentication mechanisms.<n>Driver authentication via CAN bus data offers a promising additional layer of defense-in-depth protection.<n>Existing open-access driver fingerprinting datasets suffer from critical limitations.
- Score: 1.6103615453094973
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Modern vehicles remain vulnerable to unauthorized use and theft despite traditional security measures including immobilizers and keyless entry systems. Criminals exploit vulnerabilities in Controller Area Network (CAN) bus systems to bypass authentication mechanisms, while social media trends have expanded auto theft to include recreational joyriding by underage drivers. Driver authentication via CAN bus data offers a promising additional layer of defense-in-depth protection, but existing open-access driver fingerprinting datasets suffer from critical limitations including reliance on decoded diagnostic data rather than raw CAN traffic, artificial fixed-route experimental designs, insufficient sampling rates, and lack of demographic information. This paper provides a comprehensive review of existing open-access driver fingerprinting datasets, analyzing their strengths and limitations to guide practitioners in dataset selection. We introduce the Kidmose CANid Dataset (KCID), which addresses these fundamental shortcomings by providing raw CAN bus data from 16 drivers across four vehicles, including essential demographic information and both daily driving and controlled fixed-route data. Beyond dataset contributions, we present a driver authentication anti-theft framework and implement a proof-of-concept prototype on a single-board computer. Through live road trials with an unaltered passenger vehicle, we demonstrate the practical feasibility of CAN bus-based driver authentication anti-theft systems. Finally, we explore diverse applications of KCID beyond driver authentication, including driver profiling for insurance and safety assessments, mechanical anomaly detection, young driver monitoring, and impaired driving detection. This work provides researchers with both the data and methodological foundation necessary to develop robust, deployable driver authentication systems...
Related papers
- CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
arXiv Detail & Related papers (2025-05-14T13:37:07Z) - AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN) [4.081467217340597]
The Controller Area Network (CAN) bus is a central system for managing in-vehicle communication between the electronic control units (ECUs)
CAN protocol poses security challenges due to inherent vulnerabilities, lacking encryption and authentication, which, combined with an expanding attack surface, necessitates robust security measures.
This paper considers the latest ROAD dataset, containing stealthy and sophisticated injections.
arXiv Detail & Related papers (2024-08-30T12:26:23Z) - Your Car Tells Me Where You Drove: A Novel Path Inference Attack via CAN Bus and OBD-II Data [57.22545280370174]
On Path Diagnostic - Intrusion & Inference (OPD-II) is a novel path inference attack leveraging a physical car model and a map matching algorithm.
We implement our attack on a set of four different cars and a total number of 41 tracks in different road and traffic scenarios.
arXiv Detail & Related papers (2024-06-30T04:21:46Z) - DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving [81.04174379726251]
This paper collects a comprehensive end-to-end driving dataset named DriveCoT.
It contains sensor data, control decisions, and chain-of-thought labels to indicate the reasoning process.
We propose a baseline model called DriveCoT-Agent, trained on our dataset, to generate chain-of-thought predictions and final decisions.
arXiv Detail & Related papers (2024-03-25T17:59:01Z) - Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view.<n>To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
arXiv Detail & Related papers (2023-12-13T23:06:30Z) - When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures.
We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN.
Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
arXiv Detail & Related papers (2023-06-09T14:33:26Z) - Enhancing Road Safety through Accurate Detection of Hazardous Driving
Behaviors with Graph Convolutional Recurrent Networks [0.2578242050187029]
We present a reliable Driving Behavior Detection (DBD) system based on Graph Convolutional Long Short-Term Memory Networks (GConvLSTM)
Our proposed model achieved a high accuracy of 97.5% for public sensors and an average accuracy of 98.1% for non-public sensors, indicating its consistency and accuracy in both settings.
Our findings demonstrate that the proposed system can effectively detect hazardous and unsafe driving behavior, with potential applications in improving road safety and reducing the number of accidents caused by driver errors.
arXiv Detail & Related papers (2023-05-08T21:05:36Z) - OpenDriver: An Open-Road Driver State Detection Dataset [13.756530418314227]
This paper develops a large-scale multimodal driving dataset, OpenDriver, for driver state detection.<n>The OpenDriver encompasses a total of 3,278 driving trips, with a signal collection duration spanning approximately 4,600 hours.
arXiv Detail & Related papers (2023-04-09T10:08:38Z) - X-CANIDS: Signal-Aware Explainable Intrusion Detection System for Controller Area Network-Based In-Vehicle Network [6.68111081144141]
X-CANIDS dissects the payloads in CAN messages into human-understandable signals using a CAN database.
X-CANIDS can detect zero-day attacks because it does not require any labeled dataset in the training phase.
arXiv Detail & Related papers (2023-03-22T03:11:02Z) - Computer vision based vehicle tracking as a complementary and scalable
approach to RFID tagging [0.0]
RFID tagging hampers the scalability of vehicle tracking solutions on both logistics and technical fronts.
We leverage publicly available implementations of computer vision algorithms to develop an interpretable vehicle tracking algorithm.
We evaluated the proposed method on 75 video clips of 285 vehicles from our system deployment site.
arXiv Detail & Related papers (2022-09-13T11:49:38Z) - Anomaly Detection in Intra-Vehicle Networks [0.0]
Modern vehicles are connected to a range of networks, including intra-vehicle networks and external networks.
With the loopholes in the existing traditional protocols, cyber-attacks on the vehicle network are rising drastically.
This paper discusses the security issues of the CAN bus protocol and proposes an Intrusion Detection System (IDS) that detects known attacks.
arXiv Detail & Related papers (2022-05-07T03:38:26Z) - CAN-LOC: Spoofing Detection and Physical Intrusion Localization on an
In-Vehicle CAN Bus Based on Deep Features of Voltage Signals [48.813942331065206]
We propose a security hardening system for in-vehicle networks.
The proposed system includes two mechanisms that process deep features extracted from voltage signals measured on the CAN bus.
arXiv Detail & Related papers (2021-06-15T06:12:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.