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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The project financed under the program of the Minister of Science and Higher Education under the name “Regional Initiative of Excellence” in the years 2019–2022 project number 020/RID/2018/19, the amount of financing 12,000,000 PLN.
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Nowak, J., Korytkowski, M., Scherer, R. (2019). Convolutional Recurrent Neural Networks for Computer Network Analysis. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_59
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