Botnet Detection based on Anomaly and Community Detection
Product Description
Botnet Detection based on Anomaly and Community Detection
Abstract— A novel two-stage approach for the important cyber-security problem of detecting the
presence of a botnet and identifying the compromised nodes (the bots), ideally before the botnet becomes active. The
first stage detects anomalies by leveraging large deviations of an empirical distribution. We propose two approaches
to create the empirical distribution: a flow-based approach estimating the histogram of quantized flows, and a graph-based approach estimating the degree distribution of node interaction graphs, encompassing both Erd˝os-R´enyi graphs and scale-free graphs. < final year projects >
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