Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network
Anomaly detection of multivariate time series (MTS) is crucial in industrial intelligent systems. To address the challenges of absence of anomaly labels, fast inference time, multi-source and multi-modality in anomaly detection, researchers have primarily investigated unsupervised reconstruction-dri...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10778252/ |
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| author | Jiahao Shan Donghong Cai Fang Fang Zahid Khan Pingzhi Fan |
| author_facet | Jiahao Shan Donghong Cai Fang Fang Zahid Khan Pingzhi Fan |
| author_sort | Jiahao Shan |
| collection | DOAJ |
| description | Anomaly detection of multivariate time series (MTS) is crucial in industrial intelligent systems. To address the challenges of absence of anomaly labels, fast inference time, multi-source and multi-modality in anomaly detection, researchers have primarily investigated unsupervised reconstruction-driven methods. However, the existing reconstruction-driven methods mainly focus on minimizing reconstruction errors while neglecting the importance of training methods that increase errors between normal and abnormal classes. Furthermore, accurately constructing the feature space of normal and abnormal classes during the reconstruction process remains a challenge. In this paper, we propose an innovative model, namely the confidence adversarial autoencoder (CAAE). The proposed CAAE combines a confidence network, based on window credibility judgment, with an autoencoder to provide credibility support for anomaly detection. We further introduce fake labels to provide the confidence network with a discriminative knowledge for identifying reconstructed data. Additionally, we implement the confidence adversarial training method to generate fake labels to construct an adversarial loss aiming to expand the decision boundary of anomaly scores. Extensive experimental results on publicly available time series datasets are provided to demonstrate the efficiency of our proposed CAAE. It reveals that excellent generalization ability and superior average performance are achieved on different datasets compared with the state-of-the-art methods. |
| format | Article |
| id | doaj-art-ba9222edf0de4d18a9ad7c8325cdcf77 |
| institution | OA Journals |
| issn | 2644-125X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-ba9222edf0de4d18a9ad7c8325cdcf772025-08-20T01:58:15ZengIEEEIEEE Open Journal of the Communications Society2644-125X2024-01-0157752776610.1109/OJCOMS.2024.351195110778252Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder NetworkJiahao Shan0Donghong Cai1https://orcid.org/0000-0001-5350-135XFang Fang2https://orcid.org/0000-0002-6582-6570Zahid Khan3https://orcid.org/0000-0003-4710-4010Pingzhi Fan4https://orcid.org/0000-0002-8281-6251College of Cyber Security, Jinan University, Guangzhou, ChinaCollege of Cyber Security, Jinan University, Guangzhou, ChinaDepartment of Electrical and Computer Engineering, Western University, London, ON, CanadaCollege of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi ArabiaKey Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu, ChinaAnomaly detection of multivariate time series (MTS) is crucial in industrial intelligent systems. To address the challenges of absence of anomaly labels, fast inference time, multi-source and multi-modality in anomaly detection, researchers have primarily investigated unsupervised reconstruction-driven methods. However, the existing reconstruction-driven methods mainly focus on minimizing reconstruction errors while neglecting the importance of training methods that increase errors between normal and abnormal classes. Furthermore, accurately constructing the feature space of normal and abnormal classes during the reconstruction process remains a challenge. In this paper, we propose an innovative model, namely the confidence adversarial autoencoder (CAAE). The proposed CAAE combines a confidence network, based on window credibility judgment, with an autoencoder to provide credibility support for anomaly detection. We further introduce fake labels to provide the confidence network with a discriminative knowledge for identifying reconstructed data. Additionally, we implement the confidence adversarial training method to generate fake labels to construct an adversarial loss aiming to expand the decision boundary of anomaly scores. Extensive experimental results on publicly available time series datasets are provided to demonstrate the efficiency of our proposed CAAE. It reveals that excellent generalization ability and superior average performance are achieved on different datasets compared with the state-of-the-art methods.https://ieeexplore.ieee.org/document/10778252/Multivariate time series (MTS)anomaly detectionadversarial trainingunsupervised learningautoencoder |
| spellingShingle | Jiahao Shan Donghong Cai Fang Fang Zahid Khan Pingzhi Fan Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network IEEE Open Journal of the Communications Society Multivariate time series (MTS) anomaly detection adversarial training unsupervised learning autoencoder |
| title | Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network |
| title_full | Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network |
| title_fullStr | Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network |
| title_full_unstemmed | Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network |
| title_short | Unsupervised Multivariate Time Series Data Anomaly Detection in Industrial IoT: A Confidence Adversarial Autoencoder Network |
| title_sort | unsupervised multivariate time series data anomaly detection in industrial iot a confidence adversarial autoencoder network |
| topic | Multivariate time series (MTS) anomaly detection adversarial training unsupervised learning autoencoder |
| url | https://ieeexplore.ieee.org/document/10778252/ |
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