Fast and Progressive Misbehavior Detection in Internet of Vehicles Based on Broad Learning and Incremental Learning Systems
文献类型：期刊论文
| 作者 | Wang, Xiao6,7 ; Zhu, Yushan5; Han, Shuangshuang4; Yang, Linyao3,6; Gu, Haixia7; Wang, Fei-Yue1,2,6
|
| 刊名 | IEEE INTERNET OF THINGS JOURNAL
 |
| 出版日期 | 2022-03-15 |
| 卷号 | 9期号:6页码:4788-4798 |
| 关键词 | 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 |
| ISSN号 | 2327-4662 |
| DOI | 10.1109/JIOT.2021.3109276 |
| 通讯作者 | Wang, Xiao(x.wang@ia.ac.cn) |
| 英文摘要 | 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. |
| 资助项目 | 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] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000766683600063 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/47963]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Wang, Xiao |
| 作者单位 | 1.Macau Univ Sci Technol, Inst Syst Engn, Macau, Peoples R China 2.Natl Univ Def Technol, Res Ctr Mil Computat Expt & Parallel Syst Technol, Changsha 410073, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China 5.Zhejiang Univ, Sch Comp Sci, Hangzhou 310027, Peoples R China 6.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China 7.China Nucl Power Engn Co Ltd, State Key Lab Nucl Power Safety Monitoring Techno, Shenzhen 518172, Guangdong, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。