Abstract
The paper proposes a method of computer network user detection with recurrent neural networks. We use long short-term memory and gated recurrent unit neural networks. To present URLs from computer network sessions to the neural networks, we add convolutional input layers. Moreover, we transform requested URLs by one-hot character-level encoding. We show detailed analysis and comparison of the experiments with the aforementioned neural networks. The system was checked on real network data collected in a local municipal network. It can classify network users; hence, it can also detect anomalies and security compromises.
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