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Signature-based botnet detection methods identify botnets by recognizingCommand and Control (C\&C) traffic and can be ineffective for botnets that usenew and sophisticate mechanisms for such communications. To address theselimitations, we propose a novel botnet detection method that analyzes thesocial relationships among nodes. The method consists of two stages: (i)anomaly detection in an "interaction" graph among nodes using large deviationsresults on the degree distribution, and (ii) community detection in a social"correlation" graph whose edges connect nodes with highly correlatedcommunications. The latter stage uses a refined modularity measure andformulates the problem as a non-convex optimization problem for whichappropriate relaxation strategies are developed. We apply our method toreal-world botnet traffic and compare its performance with other communitydetection methods. The results show that our approach works effectively and therefined modularity measure improves the detection accuracy.

Botnet Detection using Social Graph Analysis