Deep learning method for identifying anomalies in operating computer systems

The problem of detecting anomalous behavior in large software systems can be reduced to the problem of detecting anomalies in text data streams. In this paper, we propose an approach based on a combination of deep learning (an autoencoder using convolutional neural networks and a single-layer fully connected decoder) and approaches based on the fuzzy clustering method. The solution proposed allows one to construct vector representations of groups of sequential events and identify outliers in the data using a developed layer based on fuzzy clustering and radial basis functions methods.