An adaptive fault detection model based on variational auto-encoders and unsupervised transfer learning

Aiming at the problem of insufficient generalization of fault detection in traditional machine learning, an SDN controller fault detection method based on unsupervised transfer learning is proposed. The method mainly includes two parts. (1) A Gaussian mixture variational autoencoder based on the autoregressive flow is proposed. First, the encoder and decoder of variational autocoding are improved with gated recurrent units, and the improved variational autocoding can process time series data. Secondly, the gated recurrent unit is improved by using the gravitational search algorithm, which speeds up the search of the weight of the gated recurrent unit. Further, considering that the latent space of the variational autoencoder is a single Gaussian distribution, and the complex data in reality is often too simple to be represented by a single Gaussian distribution. (2) Aiming at the problem of poor generalization of fault detection models in practical scenarios, a domain adaptive fault detection algorithm based on multi-kernel maximum mean difference and intra-class distance constraints is proposed. Map the features into the manifold space to eliminate the distortion of the features in the original space. After mapping, the distance between fields needs to be measured, and the maximum mean difference of a single kernel cannot determine which kernel function is more suitable for the current task in practical applications. Therefore, the maximum mean difference based on multi-core is introduced to measure between the two fields. The experimental results show that the algorithm proposed improves the accuracy about 5% compared with the previous algorithm. •An SDN controller fault detection method based on unsupervised transfer learning is proposed.•In train model, a model uses unsupervised learning by a variational autoencoder. At the same time, the posterior distribution in the variational autoencoder is reversibly transformed into a Gaussian mixture distribution by using the autoregressive flow technique.•Computing the difference between the source domain and the target domain utilizes the knowledge gained in the pretrained model for transfer.