Everyday Internet becomes more and more convenient tool for effective information search, social interaction, on-line banking, shopping, and, unfortunately, the source of many types of computer viruses. Despite some similarities, the process of spreading viruses on Internet differs significantly from well-studied epidemic processes in biological systems; and therefore, requires a more realistic modeling . In this work, we propose several epidemic models for the most frequently observed traffic on Internet, web traffic, which consists of communication flows sent/received via web-browsers by small personal computers (clients) to/from powerful data and media computer storages (servers). Specifically, we adapt models developed by Britton at el. 2007  for sexually transmitted infections and evaluate numerically these models using a subset of NetFlow data collected from a large European Internet Service Provider (ISP) over a range of 14 days. Additionally, we incorporate in the modeling several local vaccination strategies for both clients and servers , and discuss plausible work extensions in dynamic settings .
 Niels Provos, Dean McNamee, Panayiotis Mavrommatis, Ke Wang, and Nagendra Modadugu.(2007). The ghost in the browser analysis of web-based malware. In Proceedings of the first conference on First Workshop on Hot Topics in Understanding Botnets, Berkeley, CA, USA.
 Britton T., Nordvik, M.K., and Liljeros, F. (2007): Modeling sexually transmitted infections: the effect of partnership activity and number of partners on R_0. Theoretical Population Biology, 72, 389-399.
 Britton, T., Janson, S., Martin-Löf A. (2007): Graphs with specified degree distributions, simple epidemics and local vaccination strategies. Advances in Applied Probability, 39, 922-948.
 Britton, T. and Lindholm, M.: Dynamic random networks in dynamic populations. (2010) Journal of Statistical Physics.
Keywords: epidemic models; computer virus; web traffic
Biography: Natallia Katenka is a Postdoctoral Research Fellow in the Department of Mathematics and Statistics at Boston University. She received her B.S. and M.S. degrees with honors in Applied Mathematics and Computer Science from the Belarusian State University, Minsk, Belarus. While a student, she was a Research Assistant as the National Academy of Science of Belarus. She graduated with her Ph.D. in Statistics from the University of Michigan, Ann Arbor, in 2009.
Her research interests are in statistical analysis and information fusion, particularly in applications to various problems arising in the context of wireless sensor networks, internet networks, social networks, and gene/protein regulatory networks. Specifically, she worked on the problems of network design, target detection, localization, tracking, and diagnostics by wireless sensor networks for Ph.D. thesis. Natallia's most recent work focus is on characterizing the Internet and the World Wide Web traffic data and modeling of epidemic processes on inferred networks.