Generally, the botnet is one of the most dangerous threats in the network. It has number attackers in the network. The attacker consists of DDOS attack, remote attack, etc., Bots perform perform repetitive tasks automatically or on a schedule over the internet, tasks that would be too mundane or time-consuming for an actual person. But the botnets have stealthy behavior as they are very difficult to identify. These botnets have to be identified and the internet have to be protected. Also the the activity of botnets must be prevented to provide the users, a reliable service. The past of botnet detection has a transaction process which is not secure. A efficient stastical data classifier is required to train the botent preventions system. To provide the above features clustering based analysis is done. our approach can detect and profile various P2P applications rather than identifying a specific P2P application. Anomaly based detection technique is used to obtain this goal.