Your Car Tells Me Where You Drove: A Novel Path Inference Attack via CAN Bus and OBD-II Data
- URL: http://arxiv.org/abs/2407.00585v1
- Date: Sun, 30 Jun 2024 04:21:46 GMT
- Title: Your Car Tells Me Where You Drove: A Novel Path Inference Attack via CAN Bus and OBD-II Data
- Authors: Tommaso Bianchi, Alessandro Brighente, Mauro Conti, Andrea Valori,
- Abstract summary: 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.
- Score: 57.22545280370174
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Despite its well-known security issues, the Controller Area Network (CAN) is still the main technology for in-vehicle communications. Attackers posing as diagnostic services or accessing the CAN bus can threaten the drivers' location privacy to know the exact location at a certain point in time or to infer the visited areas. This represents a serious threat to users' privacy, but also an advantage for police investigations to gather location-based evidence. In this paper, we present On Path Diagnostic - Intrusion \& Inference (OPD-II), a novel path inference attack leveraging a physical car model and a map matching algorithm to infer the path driven by a car based on CAN bus data. Differently from available attacks, our approach only requires the attacker to know the initial location and heading of the victim's car and is not limited by the availability of training data, road configurations, or the need to access other victim's devices (e.g., smartphones). We implement our attack on a set of four different cars and a total number of 41 tracks in different road and traffic scenarios. We achieve an average of 95% accuracy on reconstructing the coordinates of the recorded path by leveraging a dynamic map-matching algorithm that outperforms the 75% and 89% accuracy values of other proposals while removing their set of assumptions.
Related papers
- A Location Validation Technique to Mitigate GPS Spoofing Attacks in IEEE 802.11p based Fleet Operator's Network of Electric Vehicles [2.5582913676558205]
Vehicle rebalancing application uses the GPS location data of the vehicles periodically to determine the vehicle(s) to be moved to a different charging station for rebalancing.
A malicious attacker residing in the network can spoof the GPS location data packets of the target vehicle(s) resulting in misinterpretation of the location of the vehicle(s)
We propose a location tracking technique that can validate the current location of a vehicle based on its previous location and roadmaps.
arXiv Detail & Related papers (2024-10-16T20:42:27Z) - Protecting Vehicle Location Privacy with Contextually-Driven Synthetic Location Generation [5.283624671933499]
We introduce VehiTrack, a new threat model to demonstrate the vulnerability of Geo-Ind in protecting vehicle location privacy.
VehiTrack can accurately determine exact vehicle locations from obfuscated data.
We propose TransProtect, a new geo-obfuscation approach that limits obfuscation to realistic vehicle movement patterns.
arXiv Detail & Related papers (2024-09-14T17:47:23Z) - Federated Learning-based Vehicle Trajectory Prediction against
Cyberattacks [4.0989155767548375]
This paper proposes a Federated Learning-based Vehicle Trajectory Prediction Algorithm against Cyberattacks.
The proposed FL-TP algorithm can improve cyberattack detection and trajectory prediction by up to 6.99% and 54.86%, respectively.
arXiv Detail & Related papers (2023-06-14T15:17:58Z) - DeepAccident: A Motion and Accident Prediction Benchmark for V2X
Autonomous Driving [76.29141888408265]
We propose a large-scale dataset containing diverse accident scenarios that frequently occur in real-world driving.
The proposed DeepAccident dataset includes 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset.
arXiv Detail & Related papers (2023-04-03T17:37:00Z) - Reinforcement Learning based Cyberattack Model for Adaptive Traffic
Signal Controller in Connected Transportation Systems [61.39400591328625]
In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time.
This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes.
One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network.
An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
arXiv Detail & Related papers (2022-10-31T20:12:17Z) - 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) - Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting [91.69900691029908]
We advocate for predicting both the individual motions as well as the scene occupancy map.
We propose a Scene-Actor Graph Neural Network (SA-GNN) which preserves the relative spatial information of pedestrians.
On two large-scale real-world datasets, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods.
arXiv Detail & Related papers (2021-01-07T06:08:21Z) - Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs [72.67604044776662]
We tackle the problem of of-temporal tagging of self-driving scenes from raw sensor data.
Our approach learns a universal embedding for all tags, enabling efficient tagging of many attributes and faster learning of new attributes with limited data.
arXiv Detail & Related papers (2020-11-12T02:18:16Z) - Graph-Based Intrusion Detection System for Controller Area Networks [1.697297400355883]
The controller area network (CAN) is the most widely used intra-vehicular communication network in the automotive industry.
We propose a four-stage intrusion detection system that uses the chi-squared method and can detect any kind of strong and weak cyber attacks in a CAN.
Our experimental results show that we have a very low 5.26% misclassification for denial of service (DoS) attack, 10% misclassification for fuzzy attack, 4.76% misclassification for replay attack, and no misclassification for spoofing attack.
arXiv Detail & Related papers (2020-09-24T01:33:58Z)
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.