CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories
Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System (GPS) data, which is susceptible to GPS data outage. This paper introduces CAN-Trace, a novel privacy attack mechanism that leverages Controller Area Network (CAN) messages to uncover driving trajectories, posing a significant risk to drivers' long-term privacy. A new trajectory reconstruction algorithm is proposed to transform the CAN messages, specifically vehicle speed and accelerator pedal position, into weighted graphs accommodating various driving statuses. CAN-Trace identifies driving trajectories using graph-matching algorithms applied to the created graphs in comparison to road networks. We also design a new metric to evaluate matched candidates, which allows for potential data gaps and matching inaccuracies. Empirical validation under various real-world conditions, encompassing different vehicles and driving regions, demonstrates the efficacy of CAN-Trace: it achieves an attack success rate of up to 90.59% in the urban region, and 99.41% in the suburban region.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Attribute Inference Attacks for Federated Regression Tasks
Federated Learning (FL) enables multiple clients, such as mobile phones and IoT devices, to collaboratively train a global machine learning model while keeping their data localized. However, recent studies have revealed …
AttributeFederated LearningregressionExploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN
Vehicles today comprise intelligent systems like connected autonomous driving and advanced driving assistance systems (ADAS) to enhance the driving experience, which is enabled through increased connectivity to infrastru…
Autonomous DrivingIntrusion DetectionMulti-class ClassificationFrom Models to Network Topologies: A Topology Inference Attack in Decentralized Federated Learning
Federated Learning (FL) is widely recognized as a privacy-preserving machine learning paradigm due to its model-sharing mechanism that avoids direct data exchange. Nevertheless, model training leaves exploitable traces t…
Federated LearningInference AttackPrivacy PreservingNatural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving
Vision-Language Models (VLMs) have been integrated into autonomous driving systems to enhance reasoning capabilities through tasks such as Visual Question Answering (VQA). However, the robustness of these systems against…
Autonomous DrivingBackdoor AttackLanguage ModelingLanguage Modelling+3CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks Simulation
Modern vehicles are increasingly vulnerable to attacks that exploit network infrastructures, particularly the Controller Area Network (CAN) networks. To effectively counter such threats using contemporary tools like Intr…
Intrusion Detection