A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset
- URL: http://arxiv.org/abs/2012.14600v3
- Date: Wed, 7 Feb 2024 14:04:43 GMT
- Title: A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset
- Authors: Miki E. Verma and Robert A. Bridges and Michael D. Iannacone and
Samuel C. Hollifield and Pablo Moriano and Steven C. Hespeler and Bill Kay
and Frank L. Combs
- Abstract summary: Controller Area Networks (CANs) lack basic security properties and are easily exploitable.
producing vehicular CAN data with a variety of intrusions is out of reach for most researchers.
We present the first comprehensive guide to the existing open CAN intrusion datasets.
- Score: 1.6494191187996927
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lack
basic security properties and are easily exploitable. A rapidly growing field
of CAN security research has emerged that seeks to detect intrusions on CANs.
Producing vehicular CAN data with a variety of intrusions is out of reach for
most researchers as it requires expensive assets and expertise. To assist
researchers, we present the first comprehensive guide to the existing open CAN
intrusion datasets, including a quality analysis of each dataset and an
enumeration of each's benefits, drawbacks, and suggested use case. Current
public CAN IDS datasets are limited to real fabrication (simple message
injection) attacks and simulated attacks often in synthetic data, which lack
fidelity. In general, the physical effects of attacks on the vehicle are not
verified in the available datasets. Only one dataset provides signal-translated
data but not a corresponding raw binary version. Overall, the available data
pigeon-holes CAN IDS works into testing on limited, often inappropriate data
(usually with attacks that are too easily detectable to truly test the method),
and this lack data has stymied comparability and reproducibility of results. As
our primary contribution, we present the ROAD (Real ORNL Automotive
Dynamometer) CAN Intrusion Dataset, consisting of over 3.5 hours of one
vehicle's CAN data. ROAD contains ambient data recorded during a diverse set of
activities, and attacks of increasing stealth with multiple variants and
instances of real fuzzing, fabrication, and unique advanced attacks, as well as
simulated masquerade attacks. To facilitate benchmarking CAN IDS methods that
require signal-translated inputs, we also provide the signal time series format
for many of the CAN captures. Our contributions aim to facilitate appropriate
benchmarking and needed comparability in the CAN IDS field.
Related papers
- UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction [93.77809355002591]
We introduce UniTraj, a comprehensive framework that unifies various datasets, models, and evaluation criteria.
We conduct extensive experiments and find that model performance significantly drops when transferred to other datasets.
We provide insights into dataset characteristics to explain these findings.
arXiv Detail & Related papers (2024-03-22T10:36:50Z) - MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature
Drone Threats [37.981623262267036]
MMAUD addresses a critical gap in contemporary threat detection methodologies by focusing on drone detection, UAV-type classification, and trajectory estimation.
It offers a unique overhead aerial detection vital for addressing real-world scenarios with higher fidelity than datasets captured on specific vantage points using thermal and RGB.
Our proposed modalities are cost-effective and highly adaptable, allowing users to experiment and implement new UAV threat detection tools.
arXiv Detail & Related papers (2024-02-06T04:57:07Z) - Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results [73.98594459933008]
Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems.
This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets.
We introduce the Wild Face Anti-Spoofing dataset, a large-scale, diverse FAS dataset collected in unconstrained settings.
arXiv Detail & Related papers (2023-04-12T10:29:42Z) - 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) - Supervised Contrastive ResNet and Transfer Learning for the In-vehicle
Intrusion Detection System [0.22843885788439797]
We propose a novel deep learning model called supervised contrastive (SupCon) ResNet to handle multiple attack identification on the CAN bus.
The model improves the overall false-negative rates of four types of attack by four times on average, compared to other models.
The model achieves the highest F1 score at 0.9994 on the survival dataset by utilizing transfer learning.
arXiv Detail & Related papers (2022-07-18T05:34:55Z) - CANShield: Signal-based Intrusion Detection for Controller Area Networks [29.03951113836835]
We propose CANShield, a signal-based intrusion detection framework for the CAN bus.
CanShield consists of three modules: a data preprocessing module that handles the high-dimensional CAN data stream at the signal level; a data analyzer module consisting of multiple deep autoencoder networks, each analyzing the time-series data from a different temporal perspective; and an attack detection module that uses an ensemble method to make the final decision.
arXiv Detail & Related papers (2022-05-03T04:52:44Z) - Unsupervised Domain Adaptive Learning via Synthetic Data for Person
Re-identification [101.1886788396803]
Person re-identification (re-ID) has gained more and more attention due to its widespread applications in video surveillance.
Unfortunately, the mainstream deep learning methods still need a large quantity of labeled data to train models.
In this paper, we develop a data collector to automatically generate synthetic re-ID samples in a computer game, and construct a data labeler to simultaneously annotate them.
arXiv Detail & Related papers (2021-09-12T15:51:41Z) - DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly
detection in air transportation [68.8204255655161]
We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE)
It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase.
Results show that the DAE achieves better results in both accuracy and speed of detection.
arXiv Detail & Related papers (2021-09-08T14:07:55Z) - Time-Based CAN Intrusion Detection Benchmark [0.0]
Vehicle control systems are vulnerable to message injection attacks.
Time-based intrusion detection systems (IDSs) have been proposed to detect these messages.
We benchmark four time-based IDSs against the newly published ROAD dataset.
We also develop an after-market plug-in detector using lightweight hardware.
arXiv Detail & Related papers (2021-01-14T18:33:19Z) - Data Mining with Big Data in Intrusion Detection Systems: A Systematic
Literature Review [68.15472610671748]
Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation.
The rapid rate and volume of data creation has begun to pose significant challenges for data management and security.
The design and deployment of intrusion detection systems (IDS) in the big data setting has, therefore, become a topic of importance.
arXiv Detail & Related papers (2020-05-23T20:57:12Z) - Stance Detection Benchmark: How Robust Is Your Stance Detection? [65.91772010586605]
Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim.
We introduce a StD benchmark that learns from ten StD datasets of various domains in a multi-dataset learning setting.
Within this benchmark setup, we are able to present new state-of-the-art results on five of the datasets.
arXiv Detail & Related papers (2020-01-06T13:37:51Z)
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.