IoV Dataset 2024 | Datasets | Research | Canadian Institute for Cybersecurity | UNB

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Canadian Institute for Cybersecurity
CIC

CIC IoV dataset 2024

Advancing Realistic IDS Approaches against DoS and Spoofing Attack in IoV CAN bus

The main goal of this research is to propose a realistic benchmark dataset to support the development of new cybersecurity solutions for internet of vehicles (IoV) operations. To accomplish this, five attacks were executed against the fully intact inner structure of a 2019 Ford car, complete with all Electronic Control Units (ECUs). However, the vehicle was rendered immobile and incapable of causing any potential harm or injuries. Hence, all attacks were carried out on the vehicle without endangering the car's driver or passengers.

These attacks are classified as spoofing and Denial-of-Service (DoS) and were carried out through the CAN-BUS protocol. This effort establishes a baseline complementary to existing contributions and supports researchers to propose new IoV solutions to strengthen the overall security using different techniques (e.g., Machine Learning - ML).

The main contributions are:

  • This research focuses on addressing the critical gap in cybersecurity for IoVs by developing a comprehensive security dataset called CICIoV2024. The dataset is derived from extensive experiments conducted on the ECUs of a 2019 Ford vehicle, and provides a detailed view of intra-vehicular communications. This dataset can be used as a benchmark to advance cybersecurity solutions in IoV.
  • The paper offers a comprehensive examination of different Machine Learning (ML) techniques. It showcases the effectiveness of these algorithms in identifying, avoiding, and reducing cyber-attacks in IoV systems. This analysis is crucial in improving the understanding and application of ML in the field of IoV cybersecurity.
  • The research on Foundation for Future IoV Security sets a new benchmark in the field of IoV security. This paves the way for future explorations and opens up avenues for further optimization of ML models. The research also enables a deeper analysis of features in IoV cybersecurity and facilitates the integration of the CICIoV2024 dataset with broader smart city systems. By laying the groundwork for developing diverse datasets focusing on different vehicle models, it creates a strong foundation for future research on IoV security.

Data description

Features extracted for the CICIoV2024 dataset:

Feature name Description
ID Arbitration - indicates the priority of the message and the type of data it carries.
DATA_0 Byte 0 of the data transmitted.
DATA_1 Byte 1 of the data transmitted.
DATA_2 Byte 2 of the data transmitted.
DATA_3 Byte 3 of the data transmitted.
DATA_4 Byte 4 of the data transmitted.
DATA_5 Byte 5 of the data transmitted.
DATA_6 Byte 6 of the data transmitted.
DATA_7 Byte 7 of the data transmitted.
label The identification of benign or malicious traffic.
category The identification of the category to which the traffic belongs.
specific_class The identification of the specific class of the traffic.

 

Decimal representation:

mean std min 25% 50% 75% max
ID 537.207946 322.479994 65 357 516 578 1438
DATA_0 71.0865995 88.9771748 0 0 16 127 255
DATA_1 69.9892503 95.5837431 0 0 12 128 255
DATA_2 55.0112724 72.7658378 0 0 13 125 255
DATA_3 57.4536383 90.3207664 0 0 0 92 255
DATA_4 45.2851673 64.4583498 0 0 6 86 255
DATA_5 53.8826134 94.3361202 0 0 0 63 255
DATA_6 71.7491441 101.687183 0 0 0 138 255
DATA_7 60.2747691 99.9654672 0 0 0 80 255

 

Binary representation:

mean std min 25% 50% 75% max
ID0 0 0 0 0 0 0 0
ID1 0 0 0 0 0 0 0
ID2 0 0 0 0 0 0 0
ID3 0 0 0 0 0 0 0
ID4 0 0 0 0 0 0 0
ID5 0 0 0 0 0 0 0
ID6 0.16952406 0.37521428 0 0 0 0 1
ID7 0.42718498 0.49466973 0 0 0 1 1
ID8 0.29153065 0.45446747 0 0 0 1 1
ID9 0.13162299 0.33808056 0 0 0 0 1
ID10 0.37840989 0.48499074 0 0 0 1 1
ID11 0.50427242 0.49998192 0 0 1 1 1
ID12 0.48455602 0.4997616 0 0 0 1 1
ID13 0.19254321 0.39429739 0 0 0 0 1
ID14 0.5746173 0.49440108 0 0 1 1 1
ID15 0.46847259 0.49900521 0 0 0 1 1
ID16 0.53340638 0.49888294 0 0 1 1 1
DATA_00 0 0 0 0 0 0 0
DATA_01 0 0 0 0 0 0 0
DATA_02 0 0 0 0 0 0 0
DATA_03 0 0 0 0 0 0 0
DATA_04 0 0 0 0 0 0 0
DATA_05 0 0 0 0 0 0 0
DATA_06 0 0 0 0 0 0 0
DATA_07 0 0 0 0 0 0 0
DATA_08 0 0 0 0 0 0 0
DATA_09 0.22309243 0.41631998 0 0 0 0 1
DATA_010 0.34454158 0.47521873 0 0 0 1 1
DATA_011 0.31769774 0.4655814 0 0 0 1 1
DATA_012 0.33472706 0.47189513 0 0 0 1 1
DATA_013 0.31426362 0.46422209 0 0 0 1 1
DATA_014 0.37693711 0.48461912 0 0 0 1 1
DATA_015 0.32008942 0.46651081 0 0 0 1 1
DATA_016 0.29611019 0.45654035 0 0 0 1 1
DATA_10 0 0 0 0 0 0 0
DATA_11 0 0 0 0 0 0 0
DATA_12 0 0 0 0 0 0 0
DATA_13 0 0 0 0 0 0 0
DATA_14 0 0 0 0 0 0 0
DATA_15 0 0 0 0 0 0 0
DATA_16 0 0 0 0 0 0 0
DATA_17 0 0 0 0 0 0 0
DATA_18 0 0 0 0 0 0 0
DATA_19 0.28475827 0.45129939 0 0 0 1 1
DATA_110 0.24356439 0.42923293 0 0 0 0 1
DATA_111 0.28651083 0.45213109 0 0 0 1 1
DATA_112 0.24899749 0.43243251 0 0 0 0 1
DATA_113 0.31631799 0.46503879 0 0 0 1 1
DATA_114 0.35077854 0.47721391 0 0 0 1 1
DATA_115 0.28126236 0.44961538 0 0 0 1 1
DATA_116 0.30358204 0.45980445 0 0 0 1 1
DATA_20 0 0 0 0 0 0 0
DATA_21 0 0 0 0 0 0 0
DATA_22 0 0 0 0 0 0 0
DATA_23 0 0 0 0 0 0 0
DATA_24 0 0 0 0 0 0 0
DATA_25 0 0 0 0 0 0 0
DATA_26 0 0 0 0 0 0 0
DATA_27 0 0 0 0 0 0 0
DATA_28 0 0 0 0 0 0 0
DATA_29 0.16682206 0.37281706 0 0 0 0 1
DATA_210 0.23185811 0.42203902 0 0 0 0 1
DATA_211 0.30791589 0.46163172 0 0 0 1 1
DATA_212 0.25489004 0.435579955 0 0 0 1 1
DATA_213 0.33766694 0.47291451 0 0 0 1 1
DATA_214 0.30989072 0.4624485 0 0 0 1 1
DATA_215 0.30772841 0.46155367 0 0 0 1 1
DATA_216 0.33122476 0.47065388 0 0 0 1 1
DATA_30 0 0 0 0 0 0 0
DATA_31 0 0 0 0 0 0 0
DATA_32 0 0 0 0 0 0 0
DATA_33 0 0 0 0 0 0 0
DATA_34 0 0 0 0 0 0 0
DATA_35 0 0 0 0 0 0 0
DATA_36 0 0 0 0 0 0 0
DATA_37 0 0 0 0 0 0 0
DATA_38 0 0 0 0 0 0 0
DATA_39 0.1918338 0.39374319 0 0 0 0 1
DATA_310 0.25849389 0.43780696 0 0 0 1 1
DATA_311 0.23691912 0.4251924 0 0 0 0 1
DATA_312 0.29121678 0.45432336 0 0 0 1 1
DATA_313 0.2679498 0.44289146 0 0 0 1 1
DATA_314 0.29034333 0.45392095 0 0 0 1 1
DATA_315 0.26623629 0.44198944 0 0 0 1 1
DATA_316 0.27697894 0.44750614 0 0 0 1 1
DATA_40 0 0 0 0 0 0 0
DATA_41 0 0 0 0 0 0 0
DATA_42 0 0 0 0 0 0 0
DATA_43 0 0 0 0 0 0 0
DATA_44 0 0 0 0 0 0 0
DATA_45 0 0 0 0 0 0 0
DATA_46 0 0 0 0 0 0 0
DATA_47 0 0 0 0 0 0 0
DATA_48 0 0 0 0 0 0 0
DATA_49 0.11576182 0.31993921 0 0 0 0 1
DATA_410 0.25658935 0.43675095 0 0 0 1 1
DATA_411 0.18494851 0.38825593 0 0 0 0 1
DATA_412 0.23620332 0.42474868 0 0 0 0 1
DATA_413 0.2521845 0.4346675 0 0 0 1 1
DATA_414 0.30246503 0.45932569 0 0 0 1 1
DATA_415 0.40185582 0.49027328 0 0 0 1 1
DATA_416 0.31728233 0.46541853 0 0 0 1 1
DATA_50 0 0 0 0 0 0 0
DATA_51 0 0 0 0 0 0 0
DATA_52 0 0 0 0 0 0 0
DATA_53 0 0 0 0 0 0 0
DATA_54 0 0 0 0 0 0 0
DATA_55 0 0 0 0 0 0 0
DATA_56 0 0 0 0 0 0 0
DATA_57 0 0 0 0 0 0 0
DATA_58 0 0 0 0 0 0 0
DATA_59 0.17389412 0.37901854 0 0 0 0 1
DATA_510 0.24749915 0.43155933 0 0 0 0 1
DATA_511 0.22956799 0.42055517 0 0 0 0 1
DATA_512 0.22830185 0.4197383 0 0 0 0 1
DATA_513 0.30647222 0.46102836 0 0 0 1 1
DATA_514 0.31337526 0.46386567 0 0 0 1 1
DATA_515 0.33985907 0.47366132 0 0 0 1 1
DATA_516 0.400219 0.48994278 0 0 0 1 1
DATA_60 0 0 0 0 0 0 0
DATA_61 0 0 0 0 0 0 0
DATA_62 0 0 0 0 0 0 0
DATA_63 0 0 0 0 0 0 0
DATA_64 0 0 0 0 0 0 0
DATA_65 0 0 0 0 0 0 0
DATA_66 0 0 0 0 0 0 0
DATA_67 0 0 0 0 0 0 0
DATA_68 0 0 0 0 0 0 0
DATA_69 0.26563695 0.44167194 0 0 0 1 1
DATA_610 0.29937602 0.4579849 0 0 0 1 1
DATA_611 0.32685967 0.46906565 0 0 0 1 1
DATA_612 0.24077505 0.42755416 0 0 0 0 1
DATA_613 0.28923981 0.45340963 0 0 0 1 1
DATA_614 0.23674372 0.42508382 0 0 0 0 1
DATA_615 0.35568971 0.47872195 0 0 0 1 1
DATA_616 0.30336617 0.45971218 0 0 0 1 1
DATA_70 0 0 0 0 0 0 0
DATA_71 0 0 0 0 0 0 0
DATA_72 0 0 0 0 0 0 0
DATA_73 0 0 0 0 0 0 0
DATA_74 0 0 0 0 0 0 0
DATA_75 0 0 0 0 0 0 0
DATA_76 0 0 0 0 0 0 0
DATA_77 0 0 0 0 0 0 0
DATA_78 0 0 0 0 0 0 0
DATA_79 0.22083781 0.41481152 0 0 0 0 1
DATA_710 0.25788674 0.43747149 0 0 0 1 1
DATA_711 0.25140266 0.43381966 0 0 0 1 1
DATA_712 0.25464434 0.43566126 0 0 0 1 1
DATA_713 0.22435147 0.41715466 0 0 0 0 1
DATA_714 0.22010852 0.41431978 0 0 0 0 1
DATA_715 0.23915456 0.42656745 0 0 0 0 1
DATA_716 0.23002885 0.42085117 0 0 0 0 1

 

Dataset directories

The main CICIoV2024 dataset directory contains four subdirectories related to three different files, namely:

  1. Hexadecimal: Data collected represented in hexadecimal format (benign, DoS, spoofing-GAS, spoofing-RPM, spoofing-SPEED, and spoofing-STEERING_WHEEL).
  2. Decimal: Data collected represented in decimal format (benign, DoS, spoofing-GAS, spoofing-RPM, spoofing-SPEED, and spoofing-STEERING_WHEEL).
  3. Binary: Data collected represented in binary format (benign, DoS, spoofing-GAS, spoofing-RPM, spoofing-SPEED, and spoofing-STEERING_WHEEL).

Acknowledgments

The authors graciously acknowledge the support from the Canadian Institute for Cybersecurity, the funding support from the National Research Council of Canada (NRC) through the AI for Logistics collaborative program, the NSERC Discovery Grant (no. RGPIN 231074), and to Dr. A. A. Ghorbani, Tier 1 Canada Research Chair.

Using the dataset

Webinar explanation about CIC IoT datasets: "From Profiling to Protection: Leveraging Datasets for Enhanced IoT Security" by Dr. Sajjad Dadkhah, Assistant Professor and Cybersecurity R&D Team Lead with Q&A by Sumit Kundu.

Citation

E. C. P. Neto, H. Taslimasa, S. Dadkhah, S. Iqbal, P. Xiong, T. Rahmanb, and A. A. Ghorbani, "CICIoV2024: Advancing Realistic IDS Approaches against DoS and Spoofing Attack in IoV CAN bus," Internet of Things, 101209, 2024.

Download the dataset