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Adversarial training of LSTM-ED based anomaly detection for complex time-series in cyber-physical-social systems.
- Source :
-
Pattern Recognition Letters . Dec2022, Vol. 164, p132-139. 8p. - Publication Year :
- 2022
-
Abstract
- • Solving the time-series anomaly detection problem in CPSSs by deep learning approach. • Combining the advantages of LSTM and reconstruction models for time- series anomaly detection problems. • Proposing the adversarial training method to improve the robustness of encoder-decoder model. • Avoiding the heavy workload of traditional methods for a priori knowledge and quality labeling. • Conducting the extensive experiments to prove the validity of the method in this paper. With the development and maturity of smart cities, more and more Cyber-Physical-Social Systems (CPSSs) need to monitor a variety of time-series data from sensors and network transmissions to ensure the quality and reliability of the Cyber-Physical-Social Services. Time-series anomaly detection is a common search problem in the field of pattern recognition. Existing approaches and models of anomaly detection have solved the problem of simple smooth time-series and perform ideal recognition performance. However, in real scenarios, complex time-series with non-Gaussian noise and complex data distributions are prevalent. Compared to smooth and simple time-series, complex time-series occur more frequently in real-world settings and are difficult to model and label. To address these challenges, this paper proposes an unsupervised anomaly detection algorithm based on Long Short-Term Memory Encoder-Decoder (LSTM-ED) via an adversarial training method for complex time-series in cyber-physical-social systems with high performance. This is a novel method that incorporates adversarial learning to improve the robustness of encoder-decoder architecture, enabling it to obtain good anomaly detection results for complex time-series. In addition, LSTM is employed as the network unit of encoder-decoder architecture, which is also able to extract temporal correlation in time-series to a greater extent. We have conducted extensive experiments on four datasets from real scenarios and the results show that the accuracy of the proposed adversarial training of LSTM-ED is significantly better than that of the state-of-the-art methods, including other unsupervised methods and traditional supervised methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 164
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 160631388
- Full Text :
- https://doi.org/10.1016/j.patrec.2022.10.017