Anomaly detection of cloud virtual machine based on LSTM-GRBM

A model of Long Short Term Memory and Gaussian Boltzmann Machine Anomaly Detection(LsGrbmAd) is proposed in this paper. Firstly, the model captures the timing characteristics of cloud virtual machine performance indicators through long short term memory LSTM), and uses Dropout to prevent data overfitting. Secondly, the free energy is obtained by using The Gaussian Boltzmann machine (GRBM). Finally, the free energy obtained is compared with the parameter benchmark model obtained in the training stage to judge whether there are abnormalities in the cloud virtual machine. The experiment shows that the model can detect the abnormity of the cloud virtual machine, and the accuracy is improved greatly.