An Anomaly Detection Model Based on CNN-VAE for IoT Devices

Anomaly Detection on IoT devices is a widely studied task in industry. As deep learning methods developed, they have been a prevailing solution to this task, especially in the certain working condition of lacking anomaly samples. Among all the methods, for complicated data with changing period and transmitting noise in the real production environments, VAE appears to be potential. We first reconstruct the 1D time series to 2D tensors as it is hard to apply normal data augmentation methods on a time series dataset with continuous semantic. Then we used a CNN-VAE model, an improved reconstruction-based anomaly detection method, to compute the reconstruction error in an unsupervised way. The method can be integrated to an end-to-end framework as it is lightweight. Comparing with other anomaly detection methods on our dataset, our method showed the best results.