Abstract
In this paper, we present a novel system to detect abnormal behaviour of computer network users 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). There are many causes of an abnormal behaviour of network users e.g. a computer can be infected by a virus or a Trojan, a stranger can take control of a computer system, etc. Thus, the proposed system can be a very important security mechanism in networks. The system can be also used to make personal user profiles. We use the bag-of-words model to analyse the text data from firewall logs from 63 users collected over a one and half month period. The 500 GB of the network traffic meta-data allowed to achieve satisfactory classification accuracy.
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Acknowledgments
The research presented in this paper was performed within a project number RPLD.01.02.02-10-0108/17, financed by the Regional Operational Programme for Łódzkie Voivodeship 2014–2020.
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Nowak, J., Korytkowski, M., Nowicki, R., Scherer, R., Siwocha, A. (2018). Random Forests for Profiling Computer Network Users. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_64
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DOI: https://doi.org/10.1007/978-3-319-91262-2_64
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