File size: 2,853 Bytes
649c6dc 11afe6f 649c6dc 11afe6f 649c6dc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
---
license: apache-2.0
task_categories:
- text-classification
- feature-extraction
language:
- en
tags:
- code
pretty_name: Multi Fuzzer CAN dataset Simulation
size_categories:
- 10M<n<100M
---
# Dataset Card for Multi-FuzzerCAN
The Controller Area Network (CAN) is crucial for automotive safety, yet remains vulnerable
to various fuzzing attacks that can compromise vehicle operations. This paper presents a comprehensive
detection framework that identifies both common CAN vulnerabilities (DoS, Spoofing, Replay, and general
Fuzzing) and specific fuzzer attack types (identity, replay, random, brute force, and mutation-based) using
deep learning-based models. We evaluate four recurrent neural network architectures, including standard
RNN, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit
(GRU), across three CAN datasets: the publicly available Hacking Car and OTIDS datasets, along with our
proprietary Multi-FuzzerCAN dataset. To address the inherent class imbalance in attack data, we implement
targeted random oversampling and random undersampling techniques tailored to each dataset’s distribution
characteristics, significantly improving model performance. Our experimental results demonstrate that all
four models achieve exceptional accuracy, recall, precision, and F1 scores across all datasets, with LSTM and
BiLSTM architectures consistently outperforming other approaches. These findings establish the viability
of combining deep learning models with data balancing techniques to enhance the resilience of automotive
CAN networks against diverse cyber threats, especially multi-fuzzer attacks.
#### Data Collection and Processing
Our private dataset is simulated and collected from GearGoat and CarringCaribou for multi-fuzzing types on CAN virtual without physical settings needed.
We provide both dataset structures: (1) raw data simulation and collection without labeling data; (2) labeling processing data
## Citation
T. -T. -H. Le, Y. Hwang, J. Son and H. Kim, "Leverage Sampling Methods and Deep Neural Networks for Fuzzer CAN Bus Message Detection," in IEEE Access, doi: 10.1109/ACCESS.2025.3572573.
**BibTeX:**
@ARTICLE{11009200,
author={Le, Thi-Thu-Huong and Hwang, Yeonjeong and Son, JunYoung and Kim, Howon},
journal={IEEE Access},
title={Leverage Sampling Methods and Deep Neural Networks for Fuzzer CAN Bus Message Detection},
year={2025},
volume={},
number={},
pages={1-1},
keywords={Fuzzing;Controller area networks;Accuracy;Automotive engineering;Intrusion detection;Computer crime;Deep learning;Electronic mail;Data models;Computer architecture;Anomaly Detection;Automotive Vulnerability;Controller Area Network;Deep Neural Networks;Fuzzy Attack;Sampling Data},
doi={10.1109/ACCESS.2025.3572573}}
## Dataset Card Contact
Email: [email protected] |