A Three Stage Botnet Detection Technique using Random and Obrazom Graph s

This paper proposes three-stage botnet detection technique based on the anomaly and community detection. The first stage is a pragmatic node based distributed approach of sparse graph sequences. The second stage detects the bot from sparse matrix and correlations of interactions among the node. In the third stage, random graph is evaluating the performance of the bots and verified with both odd and even types of nodes. The same is extended and verified through Obrazom triple connected graphs. This verification is helpful to identify the aggressive bots through the optimized pivotal nodes. Machine Learning based Botnet Detection techniques are implemented in various levels like centralized and distributed level of networks. We can apply this three-stage bot detection in large-scale data.