Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of Vehicles Article Swipe
Wanting Gou
,
Haodi Zhang
,
Ronghui Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23218788
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23218788
The Internet of Vehicles(IoV) employs vehicle-to-everything (V2X) technology to establish intricate interconnections among the Internet, the IoT network, and the Vehicle Networks (IVNs), forming a complex vehicle communication network. However, the vehicle communication network is very vulnerable to attacks. The implementation of an intrusion detection system (IDS) emerges as an essential requisite to ensure the security of in-vehicle/inter-vehicle communication in IoV. Within this context, the imbalanced nature of network traffic data and the diversity of network attacks stand as pivotal factors in IDS performance. On the one hand, network traffic data often heavily suffer from data imbalance, which impairs the detection performance. To address this issue, this paper employs a hybrid approach combining the Synthetic Minority Over-sampling Technique (SMOTE) and RandomUnderSampler to achieve a balanced class distribution. On the other hand, the diversity of network attacks constitutes another significant factor contributing to poor intrusion detection model performance. Most current machine learning-based IDSs mainly perform binary classification, while poorly dealing with multiclass classification. This paper proposes an adaptive tree-based ensemble network as the intrusion detection engine for the IDS in IoV. This engine employs a deep-layer structure, wherein diverse ML models are stacked as layers and are interconnected in a cascading manner, which enables accurate and efficient multiclass classification, facilitating the precise identification of diverse network attacks. Moreover, a machine learning-based approach is used for feature selection to reduce feature dimensionality, substantially alleviating the computational overhead. Finally, we evaluate the proposed IDS performance on various cyber-attacks from the in-vehicle and external networks in IoV by using the network intrusion detection dataset CICIDS2017 and the vehicle security dataset Car-Hacking. The experimental results demonstrate remarkable performance, with an F1-score of 0.965 on the CICIDS2017 dataset and an F1-score of 0.9999 on the Car-Hacking dataset. These scores demonstrate that our IDS can achieve efficient and precise multiclass classification. This research provides a valuable reference for ensuring the cybersecurity of IoV.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23218788
- https://www.mdpi.com/1424-8220/23/21/8788/pdf?version=1698490479
- OA Status
- gold
- Cited By
- 26
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388019674
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388019674Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23218788Digital Object Identifier
- Title
-
Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of VehiclesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-28Full publication date if available
- Authors
-
Wanting Gou, Haodi Zhang, Ronghui ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/s23218788Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/23/21/8788/pdf?version=1698490479Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/23/21/8788/pdf?version=1698490479Direct OA link when available
- Concepts
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Computer science, Intrusion detection system, Feature selection, Machine learning, Overhead (engineering), Data mining, Artificial intelligence, Context (archaeology), Ensemble learning, Network security, Computer network, Biology, Operating system, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
26Total citation count in OpenAlex
- Citations by year (recent)
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2025: 15, 2024: 10, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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54Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ML | 188 |
| abstract_inverted_index.On | 84, 127 |
| abstract_inverted_index.To | 102 |
| abstract_inverted_index.an | 42, 49, 165, 274, 283 |
| abstract_inverted_index.as | 48, 78, 170, 192 |
| abstract_inverted_index.by | 253 |
| abstract_inverted_index.in | 59, 81, 178, 197, 251 |
| abstract_inverted_index.is | 34, 221 |
| abstract_inverted_index.of | 2, 41, 56, 67, 74, 133, 212, 276, 285, 314 |
| abstract_inverted_index.on | 242, 278, 287 |
| abstract_inverted_index.to | 8, 37, 52, 121, 141, 226 |
| abstract_inverted_index.we | 236 |
| abstract_inverted_index.IDS | 82, 177, 240, 296 |
| abstract_inverted_index.IoT | 16 |
| abstract_inverted_index.IoV | 252 |
| abstract_inverted_index.The | 0, 39, 267 |
| abstract_inverted_index.and | 18, 71, 119, 194, 204, 248, 261, 282, 300 |
| abstract_inverted_index.are | 190, 195 |
| abstract_inverted_index.can | 297 |
| abstract_inverted_index.for | 175, 223, 310 |
| abstract_inverted_index.one | 86 |
| abstract_inverted_index.our | 295 |
| abstract_inverted_index.the | 13, 15, 19, 30, 54, 64, 72, 85, 99, 113, 128, 131, 171, 176, 209, 232, 238, 246, 255, 262, 279, 288, 312 |
| abstract_inverted_index.IDSs | 151 |
| abstract_inverted_index.IoV. | 60, 179, 315 |
| abstract_inverted_index.Most | 147 |
| abstract_inverted_index.This | 162, 180, 304 |
| abstract_inverted_index.data | 70, 90, 95 |
| abstract_inverted_index.from | 94, 245 |
| abstract_inverted_index.poor | 142 |
| abstract_inverted_index.that | 294 |
| abstract_inverted_index.this | 62, 104, 106 |
| abstract_inverted_index.used | 222 |
| abstract_inverted_index.very | 35 |
| abstract_inverted_index.with | 159, 273 |
| abstract_inverted_index.(IDS) | 46 |
| abstract_inverted_index.(V2X) | 6 |
| abstract_inverted_index.0.965 | 277 |
| abstract_inverted_index.These | 291 |
| abstract_inverted_index.among | 12 |
| abstract_inverted_index.class | 125 |
| abstract_inverted_index.hand, | 87, 130 |
| abstract_inverted_index.model | 145 |
| abstract_inverted_index.often | 91 |
| abstract_inverted_index.other | 129 |
| abstract_inverted_index.paper | 107, 163 |
| abstract_inverted_index.stand | 77 |
| abstract_inverted_index.using | 254 |
| abstract_inverted_index.which | 97, 201 |
| abstract_inverted_index.while | 156 |
| abstract_inverted_index.0.9999 | 286 |
| abstract_inverted_index.Within | 61 |
| abstract_inverted_index.binary | 154 |
| abstract_inverted_index.engine | 174, 181 |
| abstract_inverted_index.ensure | 53 |
| abstract_inverted_index.factor | 139 |
| abstract_inverted_index.hybrid | 110 |
| abstract_inverted_index.issue, | 105 |
| abstract_inverted_index.layers | 193 |
| abstract_inverted_index.mainly | 152 |
| abstract_inverted_index.models | 189 |
| abstract_inverted_index.nature | 66 |
| abstract_inverted_index.poorly | 157 |
| abstract_inverted_index.reduce | 227 |
| abstract_inverted_index.scores | 292 |
| abstract_inverted_index.suffer | 93 |
| abstract_inverted_index.system | 45 |
| abstract_inverted_index.(IVNs), | 22 |
| abstract_inverted_index.(SMOTE) | 118 |
| abstract_inverted_index.Vehicle | 20 |
| abstract_inverted_index.achieve | 122, 298 |
| abstract_inverted_index.address | 103 |
| abstract_inverted_index.another | 137 |
| abstract_inverted_index.attacks | 76, 135 |
| abstract_inverted_index.complex | 25 |
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| abstract_inverted_index.dataset | 259, 265, 281 |
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| abstract_inverted_index.diverse | 187, 213 |
| abstract_inverted_index.emerges | 47 |
| abstract_inverted_index.employs | 4, 108, 182 |
| abstract_inverted_index.enables | 202 |
| abstract_inverted_index.factors | 80 |
| abstract_inverted_index.feature | 224, 228 |
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| abstract_inverted_index.heavily | 92 |
| abstract_inverted_index.impairs | 98 |
| abstract_inverted_index.machine | 149, 218 |
| abstract_inverted_index.manner, | 200 |
| abstract_inverted_index.network | 33, 68, 75, 88, 134, 169, 214, 256 |
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| abstract_inverted_index.pivotal | 79 |
| abstract_inverted_index.precise | 210, 301 |
| abstract_inverted_index.results | 269 |
| abstract_inverted_index.stacked | 191 |
| abstract_inverted_index.traffic | 69, 89 |
| abstract_inverted_index.various | 243 |
| abstract_inverted_index.vehicle | 26, 31, 263 |
| abstract_inverted_index.wherein | 186 |
| abstract_inverted_index.F1-score | 275, 284 |
| abstract_inverted_index.Finally, | 235 |
| abstract_inverted_index.However, | 29 |
| abstract_inverted_index.Internet | 1 |
| abstract_inverted_index.Minority | 115 |
| abstract_inverted_index.Networks | 21 |
| abstract_inverted_index.accurate | 203 |
| abstract_inverted_index.adaptive | 166 |
| abstract_inverted_index.approach | 111, 220 |
| abstract_inverted_index.attacks. | 38, 215 |
| abstract_inverted_index.balanced | 124 |
| abstract_inverted_index.context, | 63 |
| abstract_inverted_index.dataset. | 290 |
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| abstract_inverted_index.ensuring | 311 |
| abstract_inverted_index.evaluate | 237 |
| abstract_inverted_index.external | 249 |
| abstract_inverted_index.network, | 17 |
| abstract_inverted_index.network. | 28 |
| abstract_inverted_index.networks | 250 |
| abstract_inverted_index.proposed | 239 |
| abstract_inverted_index.proposes | 164 |
| abstract_inverted_index.provides | 306 |
| abstract_inverted_index.research | 305 |
| abstract_inverted_index.security | 55, 264 |
| abstract_inverted_index.valuable | 308 |
| abstract_inverted_index.Internet, | 14 |
| abstract_inverted_index.Moreover, | 216 |
| abstract_inverted_index.Synthetic | 114 |
| abstract_inverted_index.Technique | 117 |
| abstract_inverted_index.cascading | 199 |
| abstract_inverted_index.combining | 112 |
| abstract_inverted_index.detection | 44, 100, 144, 173, 258 |
| abstract_inverted_index.diversity | 73, 132 |
| abstract_inverted_index.efficient | 205, 299 |
| abstract_inverted_index.essential | 50 |
| abstract_inverted_index.establish | 9 |
| abstract_inverted_index.intricate | 10 |
| abstract_inverted_index.intrusion | 43, 143, 172, 257 |
| abstract_inverted_index.overhead. | 234 |
| abstract_inverted_index.reference | 309 |
| abstract_inverted_index.requisite | 51 |
| abstract_inverted_index.selection | 225 |
| abstract_inverted_index.CICIDS2017 | 260, 280 |
| abstract_inverted_index.deep-layer | 184 |
| abstract_inverted_index.imbalance, | 96 |
| abstract_inverted_index.imbalanced | 65 |
| abstract_inverted_index.in-vehicle | 247 |
| abstract_inverted_index.multiclass | 160, 206, 302 |
| abstract_inverted_index.remarkable | 271 |
| abstract_inverted_index.structure, | 185 |
| abstract_inverted_index.technology | 7 |
| abstract_inverted_index.tree-based | 167 |
| abstract_inverted_index.vulnerable | 36 |
| abstract_inverted_index.Car-Hacking | 289 |
| abstract_inverted_index.alleviating | 231 |
| abstract_inverted_index.constitutes | 136 |
| abstract_inverted_index.demonstrate | 270, 293 |
| abstract_inverted_index.performance | 241 |
| abstract_inverted_index.significant | 138 |
| abstract_inverted_index.Car-Hacking. | 266 |
| abstract_inverted_index.contributing | 140 |
| abstract_inverted_index.experimental | 268 |
| abstract_inverted_index.facilitating | 208 |
| abstract_inverted_index.performance, | 272 |
| abstract_inverted_index.performance. | 83, 101, 146 |
| abstract_inverted_index.Over-sampling | 116 |
| abstract_inverted_index.Vehicles(IoV) | 3 |
| abstract_inverted_index.communication | 27, 32, 58 |
| abstract_inverted_index.computational | 233 |
| abstract_inverted_index.cyber-attacks | 244 |
| abstract_inverted_index.cybersecurity | 313 |
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| citation_normalized_percentile.is_in_top_10_percent | True |