Abstract
A P2P botnet virus detection system based on data -mining algorithms is proposed in this study to det ect the infected computers quickly using BayesClassifier and Neural Network (NN) Classifier. The system can detect P2P botnet viruses in the early stage of infection and report to network managers to avoid further infection. The system adopts real-time flow identification techniques to detect traffic flows produced by P2P application programs and botnet viruses by comparing with the known flow patterns in the database. After trained by adjusting the system parameters using test samples, the experimental re sults show that the accuracy of BayesClassifier is 95.78% and that of NNClassifier is 98.71% in detecting P2P botnet viruses and suspected flows to achieve the goal of infection control in a short time.
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