Adversarial training of LSTM-ED based anomaly detection for complex time-series in cyber-physical-social systems

• Published in: Pattern recognition letters Vol. 164; pp. 132 - 139
• Main Authors: Zhu, Haiqi, Liu, Shaohui, Jiang, Feng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2022
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2022.10.017

•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.