Early Stage Botnet Detection and Containment via Mathematical Modeling and Prediction of Botnet Propagation Dynamics

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The research that we discuss in this technical report shows that mathematical models of botnet propagation dynamics are a viable means of detecting early stage botnet infections in an enterprise network, and thus an effective tool for containing those botnet infections in a timely fashion. The main idea that underlies this research is to localize weakly connected subgraphs within a graph that models network communications between hosts, consider those subgraphs as representatives of suspected botnets, and thus employ applied statistics to infer the underlying propagation dynamics. The inferred dynamics are materialized into a model graph, which we use within a subgraph isomorphism search process to determine whether or not there is a match between the inferred propagation dynamics and the actual propagation dynamics observed from the weakly connected subgraphs. We conduct modeling based on an intersection of statistics and graph theory such as a match between the two leads to a timely identification of infected hosts. Our mathematical modeling relies on measures of network vulnerability rates, which in this research we estimate via a statistical approach that draws on epidemiological models in biology. That estimation approach is based on random sampling and follows a novel application of statistical learning and inference in a botnet-versus-network setting. We have implemented this overall research in the Matlab and Perl programming languages, and thus have validated its effectiveness in practice in the Emulab network testbed. We have also validated the vulnerability rate estimation approach extensively with respect to realistically simulated botnet propagation dynamics in a GTNetS network simulation platform. In the technical report we describe our overall approach in detail, and thus discuss experiments along with experimental data that are indicative of the effectiveness of our overall approach to detect early stage botnet infections in an enterprise network.