Annals of Computer Science and Information Systems, Volume 15

DOI: http://dx.doi.org/10.15439/2018F321

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 501–504 (2018)

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Abstract. Automatic detection abnormal behaviour of computer network users is a desirable and hard to achieve feature. We show that convolutional neural networks are able to classify users in local computer networks based on features of web pages which were requested by a user (e.g. URL address, URL category, the day of week or time when the web page was visited). We demonstrate our approach on data collected from a firewall over an eight-month period. This network traffic meta-data allowed to achieve satisfactory classification accuracy on unseen, future network traffic data.

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