research-article

Anomaly Detection Based on RBM-LSTM Neural Network for CPS in Advanced Driver Assistance System

Authors:

Ching-Hsien Hsu,

Zunkai HuangAuthors Info & Claims

ACM Transactions on Cyber-Physical Systems, Volume 4, Issue 3

Article No.: 27, Pages 1 - 17

https://doi.org/10.1145/3377408

Published: 22 May 2020 Publication History

Editorial Notes

A corrigendum was issued for this paper on November 12, 2020. You can download the corrigendum from the supplemental material section of this citation page.

Abstract

Advanced Driver Assistance System (ADAS) is a typical Cyber Physical System (CPS) application for human–computer interaction. In the process of vehicle driving, we use the information from CPS on ADAS to not only help us understand the driving condition of the car but also help us change the driving strategies to drive in a better and safer way. After getting the information, the driver can evaluate the feedback information of the vehicle, so as to enhance the ability to assist in driving of the ADAS system. This completes a complete human–computer interaction process. However, the data obtained during the interaction usually form a large dimension, and irrelevant features sometimes hide the occurrence of anomalies, which poses a significant challenge to us to better understand the driving states of the car.

To solve this problem, we propose an anomaly detection framework based on RBM-LSTM. In this hybrid framework, RBM is trained to extract general underlying features from data collected by CPS, and LSTM is trained from the features learned by RBM. This framework can effectively improve the prediction speed and present a good prediction accuracy to show vehicle driving condition. Besides, drivers are allowed to evaluate the prediction results, so as to improve the accuracy of prediction. Through the experimental results, we can find that the proposed framework not only simplifies the training of the entire neural network and increases the training speed but also greatly improves the accuracy of the interaction-driven data analysis. It is a valid method to analyze the data generated during the human interaction.

Supplementary Material

a27-wu-corrigendum (a27-wu-corrigendum.pdf)

Corrigendum to "Anomaly Detection Based on RBM-LSTM Neural Network for CPS in Advanced Driver Assistance System" by Wu et al., ACM Transactions on Cyber-Physical Systems, Volume 4, No. 3 (TCPS 4:3).

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Cited By

Yu BXu JXiang GLin RZhao LYu Y(2024)Adaptive Anomaly Detection in Industrial Systems: An EVT-DTS Approach with LSTM Autoencoders2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)10.1109/I2MTC60896.2024.10561049(1-6)Online publication date: 20-May-2024
https://doi.org/10.1109/I2MTC60896.2024.10561049
Alfarizi MLiu JVatn JYin S(2023)Sustainability of ICPS from a Safety Perspective: Challenges and Opportunities2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE)10.1109/ISIE51358.2023.10228030(1-8)Online publication date: 19-Jun-2023
https://doi.org/10.1109/ISIE51358.2023.10228030
Guo WLi CZhu WQin W(2023)CNN-LSTM-Based Smart Field Station Electric Load Prediction2023 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC)10.1109/IIoTBDSC60298.2023.00072(1-5)Online publication date: 22-Sep-2023
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Index Terms

Anomaly Detection Based on RBM-LSTM Neural Network for CPS in Advanced Driver Assistance System
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Anomaly detection

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Published In

cover image ACM Transactions on Cyber-Physical Systems

ACM Transactions on Cyber-Physical Systems Volume 4, Issue 3

Special Issue on User-Centric Security and Safety for CPS

July 2020

279 pages

ISSN:2378-962X

EISSN:2378-9638

DOI:10.1145/3388234

Editor:
Tei-Wei Kuo
City University of Hong Kong and National Taiwan University, Taiwan

Issue’s Table of Contents

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 22 May 2020

Online AM: 07 May 2020

Accepted: 01 December 2019

Revised: 01 October 2019

Received: 01 December 2018

Published in TCPS Volume 4, Issue 3

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National Natural Science Foundation of China
Shanghai Municipal Science and Technology Commission, and National Key Research and Development Program of China

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Cited By

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https://doi.org/10.1109/I2MTC60896.2024.10561049
Alfarizi MLiu JVatn JYin S(2023)Sustainability of ICPS from a Safety Perspective: Challenges and Opportunities2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE)10.1109/ISIE51358.2023.10228030(1-8)Online publication date: 19-Jun-2023
https://doi.org/10.1109/ISIE51358.2023.10228030
Guo WLi CZhu WQin W(2023)CNN-LSTM-Based Smart Field Station Electric Load Prediction2023 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC)10.1109/IIoTBDSC60298.2023.00072(1-5)Online publication date: 22-Sep-2023
https://doi.org/10.1109/IIoTBDSC60298.2023.00072
Alajmi MMengash HAlqahtani HAljameel SHamza MSalama A(2023)Automated Threat Detection Using Flamingo Search Algorithm With Optimal Deep Learning on Cyber-Physical System EnvironmentIEEE Access10.1109/ACCESS.2023.333221311(127669-127678)Online publication date: 2023
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