TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder

With the development of communication, the Internet of Things (IoT) has been widely deployed and used in industrial manufacturing, intelligent transportation, and healthcare systems. The time-series feature of the IoT increases the data density and the data dimension, where anomaly detection is important to ensure hardware and software security. However, for the general anomaly detection methods, the anomaly may be well-reconstructed with tiny differences that are hard to discover. Measuring model complexity and the dataset feature space is a long and inefficient process. In this paper, we propose a memory-augmented autoencoder approach for detecting anomalies in IoT data, which is unsupervised, end-to-end, and not easily overgeneralized. First, a memory mechanism is introduced to suppress the generalization ability of the model, and a memory-augmented time-series autoencoder (TSMAE) is designed. Each memory item is encoded and recombined according to the similarity with the latent representation. Then, the new representation is decoded to generate the reconstructed sample, based on which the anomaly score can be obtained. Second, the addressing vector tends to be sparse by adding penalties and rectification functions to the loss. Memory modules are encouraged to extract typical normal patterns, thus inhibiting model generalization ability. Long short-term memory (LSTM) is introduced for decoding and encoding time-series data to obtain the contextual characteristics of time-series data. Finally, through experiments on the ECG and Wafer datasets, the validity of the TSMAE is verified. The rationality of the hyperparameter setting is discussed by visualizing the memory module addressing vector.