Anomaly detection method based on variational autoencoder and second-order optimized core vector machine

Aiming at the problem of low efficiency and insufficient accuracy of existing anomaly detection methods, this paper proposes a new anomaly detection model based on autoencoder and second-order optimized core vector machine (CVM_2o). First, the VAE model is used to learn the distribution characteristics and potential structure of the input data to generate an ideal data distribution. Secondly, this paper proposes a second-order bidirectional conditional gradient method, which is integrated into the traditional CVM algorithm to construct CVM_2o model, so as to achieve accurate and fast anomaly data detection. Finally, the numerical experimental results show that compared with the traditional anomaly detection method, this method has less computational time under the premise of ensuring the detection accuracy, which proves the effectiveness and practicability of the VAE-CVM_2o model in the field of anomaly detection.