Vehicle Anomaly Detection by Attention-Enhanced Temporal Convolutional Network

被引:4
作者
He, Zhitao [1 ]
Chen, Yongyi [1 ]
Zhang, Dan [1 ]
Abdulaal, Mohammed [2 ]
机构
[1] Zhejiang Univ Technol, Dept Automat, Hangzhou, Peoples R China
[2] King Abdulaziz Univ, Dept Elect & Comp Engn, Jeddah, Saudi Arabia
来源
2023 IEEE 6TH INTERNATIONAL CONFERENCE ON INDUSTRIAL CYBER-PHYSICAL SYSTEMS, ICPS | 2023年
关键词
Anomaly detection; temporal convolutional network (TCN); connected and automated vehicles (CAVs);
D O I
10.1109/ICPS58381.2023.10128090
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Intelligent transportation system (ITS) is the general trend in the field of transportation. As an important development direction to promote the realization of ITS, connected and automated vehicles (CAVs) have attracted extensive attention from many scholars. However, most CAVs system are vulnerable to various spoofing attacks. To solve this problem, an AttentionEnhanced Temporal Convolutional Network (TCN) for anomaly detection of vehicle data is proposed in this paper. Firstly, the Squeeze-and-Excitation Networks (SE-net) is used to automatically obtain the importance degree of each feature channel, and then according to this importance degree, the useful features are promoted and the features that are not useful for the current task are suppressed. Then, the multi-layer TCN model with attention branch is used to fully extract the data features, and the abnormal detection results are obtained. To verify the proposed model, we conducted experiments on the SPMD dataset. The experimental results show that Attention-Enhanced TCN has good detection performance for vehicle abnormal data, which is superior to the current state-of-the-arts.
引用
收藏
页数:6
相关论文
共 22 条
[1]   Convolutional Neural Networks for Speech Recognition [J].
Abdel-Hamid, Ossama ;
Mohamed, Abdel-Rahman ;
Jiang, Hui ;
Deng, Li ;
Penn, Gerald ;
Yu, Dong .
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2014, 22 (10) :1533-1545
[2]   MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation [J].
Abu Farha, Yazan ;
Gall, Juergen .
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, :3570-3579
[3]   Review of deep learning: concepts, CNN architectures, challenges, applications, future directions [J].
Alzubaidi, Laith ;
Zhang, Jinglan ;
Humaidi, Amjad J. ;
Al-Dujaili, Ayad ;
Duan, Ye ;
Al-Shamma, Omran ;
Santamaria, J. ;
Fadhel, Mohammed A. ;
Al-Amidie, Muthana ;
Farhan, Laith .
JOURNAL OF BIG DATA, 2021, 8 (01)
[4]   Future Directions of Intelligent Vehicles: Potentials, Possibilities, and Perspectives [J].
Cao, Dongpu ;
Wang, Xiao ;
Li, Lingxi ;
Lv, Chen ;
Na, Xiaoxiang ;
Xing, Yang ;
Li, Xuan ;
Li, Ying ;
Chen, Yuanyuan ;
Wang, Fei-Yue .
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2022, 7 (01) :7-10
[5]  
Chowdhary K., 2020, FUNDAMENTALS ARTIFIC, P603, DOI [10.1007/978-81-322-3972-7_19, DOI 10.1007/978-81-322-3972-7_19]
[6]   Soft Attention Improves Skin Cancer Classification Performance [J].
Datta, Soumyya Kanti ;
Shaikh, Mohammad Abuzar ;
Srihari, Sargur N. ;
Gao, Mingchen .
INTERPRETABILITY OF MACHINE INTELLIGENCE IN MEDICAL IMAGE COMPUTING, AND TOPOLOGICAL DATA ANALYSIS AND ITS APPLICATIONS FOR MEDICAL DATA, 2021, 12929 :13-23
[7]  
Gitman I, 2017, Arxiv, DOI arXiv:1709.08145
[8]   Dilated causal convolution with multi-head self attention for sensor human activity recognition [J].
Hamad, Rebeen Ali ;
Kimura, Masashi ;
Yang, Longzhi ;
Woo, Wai Lok ;
Wei, Bo .
NEURAL COMPUTING & APPLICATIONS, 2021, 33 (20) :13705-13722
[9]   Securing Vehicle-to-Everything (V2X) Communication Platforms [J].
Hasan, Monowar ;
Mohan, Sibin ;
Shimizu, Takayuki ;
Lu, Hongsheng .
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2020, 5 (04) :693-713
[10]   Spatial-Temporal Attention Res-TCN for Skeleton-Based Dynamic Hand Gesture Recognition [J].
Hou, Jingxuan ;
Wang, Guijin ;
Chen, Xinghao ;
Xue, Jing-Hao ;
Zhu, Rui ;
Yang, Huazhong .
COMPUTER VISION - ECCV 2018 WORKSHOPS, PT VI, 2019, 11134 :273-286