| Fast and Progressive Misbehavior Detection in Internet of Vehicles Based on Broad Learning and Incremental Learning Systems | |
Wang, Xiao1,2 ; Zhu, Yushan3; Han, Shuangshuang4; Yang, Linyao2,5; Gu, Haixia1; Wang, Fei-Yue2,6,7
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| Source Publication | IEEE INTERNET OF THINGS JOURNAL
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| ISSN | 2327-4662 |
| 2022-03-15 | |
| Volume | 9Issue:6Pages:4788-4798 |
| Corresponding Author | Wang, Xiao(x.wang@ia.ac.cn) |
| Abstract | In recent years, deep learning (DL) has been widely used in vehicle misbehavior detection and has attracted great attention due to its powerful nonlinear mapping ability. However, because of the large number of network parameters, the training processes of these methods are time consuming. Besides, the existing detection methods lack scalability; thus, they are not suitable for Internet of Vehicles (IoV) where new data are constantly generated. In this article, the concept of the broad learning system (BLS) is innovatively introduced into vehicle misbehavior detection. In order to make better use of vehicle information, key features are first extracted from the collected raw data. Then, a BLS is established, which is able to calculate the connection weight of the network efficiently and effectively by ridge regression approximation. Finally, the system can be updated and refined by an incremental learning algorithm based on the newly generated data in IoV. The experimental results show that the proposed method performs much better than DL or traditional classifiers, and could update and optimize the old model fastly and progressively while improving the system's misbehavior detection accuracy. |
| Keyword | Convolutional neural networks Feature extraction Machine learning algorithms Deep learning Global Positioning System Scalability Safety Broad learning system (BLS) incremental learning system Internet of Vehicles (IoV) misbehavior detection ridge regression approximation |
| DOI | 10.1109/JIOT.2021.3109276 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[61702519] ; Guangdong Basic and Applied Basic Research Foundation Project[2019B1515120060] ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; Science and Technology Development Fund, Macau SAR[0050/2020/A1] |
| Funding Organization | National Natural Science Foundation of China ; Guangdong Basic and Applied Basic Research Foundation Project ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV) ; Science and Technology Development Fund, Macau SAR |
| WOS Research Area | Computer Science ; Engineering ; Telecommunications |
| WOS Subject | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
| WOS ID | WOS:000766683600063 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Sub direction classification | 人工智能+交通 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/47964 |
| Collection | 复杂系统管理与控制国家重点实验室_平行智能技术与系统团队 |
| Corresponding Author | Wang, Xiao |
| Affiliation | 1.China Nucl Power Engn Co Ltd, State Key Lab Nucl Power Safety Monitoring Techno, Shenzhen 518172, Guangdong, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China 3.Zhejiang Univ, Sch Comp Sci, Hangzhou 310027, Peoples R China 4.Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 6.Natl Univ Def Technol, Res Ctr Mil Computat Expt & Parallel Syst Technol, Changsha 410073, Peoples R China 7.Macau Univ Sci Technol, Inst Syst Engn, Macau, Peoples R China |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Wang, Xiao,Zhu, Yushan,Han, Shuangshuang,et al. Fast and Progressive Misbehavior Detection in Internet of Vehicles Based on Broad Learning and Incremental Learning Systems[J]. IEEE INTERNET OF THINGS JOURNAL,2022,9(6):4788-4798. |
| APA | Wang, Xiao,Zhu, Yushan,Han, Shuangshuang,Yang, Linyao,Gu, Haixia,&Wang, Fei-Yue.(2022).Fast and Progressive Misbehavior Detection in Internet of Vehicles Based on Broad Learning and Incremental Learning Systems.IEEE INTERNET OF THINGS JOURNAL,9(6),4788-4798. |
| MLA | Wang, Xiao,et al."Fast and Progressive Misbehavior Detection in Internet of Vehicles Based on Broad Learning and Incremental Learning Systems".IEEE INTERNET OF THINGS JOURNAL 9.6(2022):4788-4798. |
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