Research on ensemble model of anomaly detection based on autoencoder

In the fields of technology such as aerospace, anomaly detection is critical to the overall system. With the large increase in data volume and dimensions, the traditional detection methods have great limitations, and thus anomaly detection algorithms based on deep learning have received widespread attention. In this paper, based on autoencoder: standard autoencoder, denoising autoencoder, and sparse autoencoder, an ensemble detection model that can extract more feature information is proposed. To make more use of these feature information, inspired by the idea of pooling layer of the CNN, two feature fusion methods are proposed. Finally, the experiment verifies that the result of this model is better than the single autoencoder model.