Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of Vehicles
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8788 |
| _version_ | 1797631313643044864 |
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| author | Wanting Gou Haodi Zhang Ronghui Zhang |
| author_facet | Wanting Gou Haodi Zhang Ronghui Zhang |
| author_sort | Wanting Gou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T11:21:02Z |
| format | Article |
| id | doaj.art-15264221cf864d708d5a8e14b1746d41 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:02Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-15264221cf864d708d5a8e14b1746d412023-11-10T15:12:04ZengMDPI AGSensors1424-82202023-10-012321878810.3390/s23218788Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of VehiclesWanting Gou0Haodi Zhang1Ronghui Zhang2China Telecom Research Institute, Guangzhou 510630, ChinaChina Telecom Research Institute, Guangzhou 510630, ChinaGuangdong Provincial Key Laboratory of Intelligent Transport System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, ChinaThe 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.https://www.mdpi.com/1424-8220/23/21/8788intrusion detection systemcybersecuritymachine learningmulticlass classificationdata balancingInternet of Vehicles |
| spellingShingle | Wanting Gou Haodi Zhang Ronghui Zhang Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of Vehicles Sensors intrusion detection system cybersecurity machine learning multiclass classification data balancing Internet of Vehicles |
| title | Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of Vehicles |
| title_full | Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of Vehicles |
| title_fullStr | Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of Vehicles |
| title_full_unstemmed | Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of Vehicles |
| title_short | Multi-Classification and Tree-Based Ensemble Network for the Intrusion Detection System in the Internet of Vehicles |
| title_sort | multi classification and tree based ensemble network for the intrusion detection system in the internet of vehicles |
| topic | intrusion detection system cybersecurity machine learning multiclass classification data balancing Internet of Vehicles |
| url | https://www.mdpi.com/1424-8220/23/21/8788 |
| work_keys_str_mv | AT wantinggou multiclassificationandtreebasedensemblenetworkfortheintrusiondetectionsystemintheinternetofvehicles AT haodizhang multiclassificationandtreebasedensemblenetworkfortheintrusiondetectionsystemintheinternetofvehicles AT ronghuizhang multiclassificationandtreebasedensemblenetworkfortheintrusiondetectionsystemintheinternetofvehicles |