Abstract: A method of identifying anomalous traffic in a sequence of computer network traffic includes preprocessing the sequence of computer network traffic into a high-dimensional time series sequence of computer network traffic, and providing the high-dimensional time series to a recurrent neural network. The recurrent neural network evaluates the provided high-dimensional time series to generate and output a predicted next element in the high-dimensional time series, which is compared with an observed actual next element in the high-dimensional time series. The observed next element in the high-dimensional time series is determined to be anomalous if it sufficiently different from the predicted next element in the high-dimensional time series.