GCRA-FWVAE: Anomaly detection for IIoT univariate time series using time-frequency domain analysis✩

Xiaoling Tao; Haowei Liu; Wenbo Zhao; Weikun Li; Yaqi Nie; Jingqi Fu

doi:10.1016/j.dcan.2025.08.007

›› 2026, Vol. 12 ›› Issue (3) :405 -416. DOI: 10.1016/j.dcan.2025.08.007

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GCRA-FWVAE: Anomaly detection for IIoT univariate time series using time-frequency domain analysis^✩

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^a Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology , Guilin 541004, Guangxi, China

^b School of Computer Science and Information Security, Guilin University of Electronic Technology , Guilin 541004, China

^* E-mail address: 21082304141@mails.guet.edu.cn (W. Zhao).

Xiaoling Tao earned the M.S. degree in Computer Application Technology (2008) and the Ph.D. degree in Information and Communication Engineering (2021) from Guilin University of Electronic Technology, Guilin, China. She joined its faculty in 2000. In 2024, she was awarded the Second Prize of the Guangxi Science and Technology Progress Award. She also served as project lead for a Guangxi Key Research and Development initiative in 2024 and acted as principal investigator for a Central-Government-Guided Local Science and Technology Development Fund project in 2023. She is currently a Professor in the School of Computer Science and Information Security at Guilin University of Electronic Technology. Her research interests include cybersecurity, cloud computing, and machine learning.

Haowei Liu earned his B.S. degree in 2022 from Guilin University of Electronic Technology in Guilin, China, and is now a third-year graduate student there. His research focuses on cyberspace security, time-series forecasting, and machine learning.

Wenbo Zhao earned his M.E. in Electronic Information from Guilin University of Electronic Technology in 2024. He is presently a Ph.D. candidate in Cyberspace Security at its School of Computer and Information Security, focusing on insider threat detection and knowledge graph methodologies.

Weikun Li received the B.S. degree in Computer Science and Technology from Guilin University of Electronic Technology in 2025. He is currently a M.S. candidate in Computer Science at the School of Computer Science, Beijing University of Posts and Telecommunications. His research interests encompass deep learning, computer vision, and natural language processing.

Yaqi Nie received his B.S. degree from Guilin University of Electronic Technology in 2023. He is currently a third-year graduate student there, researching network security and deep learning.

Jingqi Fu earned his B.Eng. from Henan University in 2024. He is currently a master’s student in Computer and Information Security at Guilin University of Electronic Science and Technology. His research spans cybersecurity, security for drones, and machine learning.

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Abstract

With the rapid development of Artificial Intelligence of Things (AIoT) technologies, the security of Industrial Internet of Things (IIoT) data faces increasing challenges, particularly in time series anomaly detection. IIoT data are typically scarce in abnormal samples and noisy, making unsupervised learning a common solution. The security challenges of IIoT data in AIoT environments require robust unsupervised anomaly detection methods. While Variational Autoencoders (VAEs) excel in noise resilience, they face two critical challenges in IIoT data: difficulties in single-variable time-series modeling and conflicts between static prior assumptions and dynamic temporal features. To address these challenges, we propose the Greater Cane Rat Algorithm-enhanced Fourier-Wavelet Conditional Variational Autoencoder (GCRA-FWVAE). Our method introduces a time-frequency dual-branch architecture that synergistically combines wavelet transforms for localized transient feature extraction and Fourier transforms for global spectral characterization. These complementary representations jointly regulate the Conditional Variational Autoencoder (CVAE) reconstruction process, effectively preserving critical anomaly signatures while suppressing noise interference. The architecture is further optimized through bioinspired Greater Cane Rat Algorithm (GCRA) to improve adaptive learning capabilities. Extensive validation on the Yahoo benchmark indicates state-of-the-art performance, achieving an F1-score of 93.6% (an improvement of 4.5% over baseline VAEs) and a precision of 95.1%. These improvements significantly increase anomaly detection accuracy and robustness, particularly in the AIoT environment, where it effectively handles more complex and dynamic industrial data.

Keywords

Anomaly detection / Time series / GCRA / CVAE / Time-frequency domain

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Xiaoling Tao, Haowei Liu, Wenbo Zhao, Weikun Li, Yaqi Nie, Jingqi Fu. GCRA-FWVAE: Anomaly detection for IIoT univariate time series using time-frequency domain analysis^✩. , 2026, 12 (3) : 405-416 DOI:10.1016/j.dcan.2025.08.007

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CRediT authorship contribution statement

Xiaoling Tao: Writing -- review & editing, Visualization, Validation, Supervision, Software, Resources, Project administration, Data curation, Conceptualization. Haowei Liu: Writing -- original draft, Visualization, Validation, Supervision, Software, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Wenbo Zhao: Writing -- review & editing, Visualization, Validation, Supervision, Data curation, Conceptualization. Weikun Li: Writing -- review & editing, Visualization, Formal analysis, Data curation, Conceptualization. Yaqi Nie: Visualization, Software, Investigation, Data curation. Jingqi Fu: Visualization, Investigation, Funding acquisition, Formal analysis, Data curation.

Declaration of competing interest

The authors declare that there is no conflict of interest regarding the publication of this manuscript. All authors have contributed to the research and preparation of the manuscript, and there are no financial, professional, or personal relationships that could be perceived as influencing the research outcomes.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 62472118), the Guangxi Science and Technology Program (No. AB24010315), the Central Guidance on Local Science and Technology Development Fund of Guangxi Province (No. ZY23055008), the Innovation Project of Guangxi Graduate Education (No. YCSW2025348), the Innovation Platform and Talent Program of Guilin City (No. 20220124-12).

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