Author(s): Wernhuar Tarng | Cheng-Kang Chou | Kuo-Liang Ou
Journal: International Journal of Computer Science & Information Technology
ISSN 0975-4660
Volume: 4;
Issue: 5;
Start page: 51;
Date: 2012;
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Keywords: Data Mining | Bayes Classifier | Neural Network | P2P Botnet | Virus Detection Systems
ABSTRACT
A P2P botnet virus detection system based on data-mining algorithms is proposed in this study to detect theinfected computers quickly using Bayes Classifier and Neural Network (NN) Classifier. The system candetect P2P botnet viruses in the early stage of infection and report to network managers to avoid furtherinfection. The system adopts real-time flow identification techniques to detect traffic flows produced by P2Papplication programs and botnet viruses by comparing with the known flow patterns in the database. Aftertrained by adjusting the system parameters using test samples, the experimental results show that theaccuracy of Bayes Classifier is 95.78% and that of NN Classifier is 98.71% in detecting P2P botnet virusesand suspected flows to achieve the goal of infection control in a short time.
Journal: International Journal of Computer Science & Information Technology
ISSN 0975-4660
Volume: 4;
Issue: 5;
Start page: 51;
Date: 2012;
VIEW PDF
DOWNLOAD PDF Original pageKeywords: Data Mining | Bayes Classifier | Neural Network | P2P Botnet | Virus Detection Systems
ABSTRACT
A P2P botnet virus detection system based on data-mining algorithms is proposed in this study to detect theinfected computers quickly using Bayes Classifier and Neural Network (NN) Classifier. The system candetect P2P botnet viruses in the early stage of infection and report to network managers to avoid furtherinfection. The system adopts real-time flow identification techniques to detect traffic flows produced by P2Papplication programs and botnet viruses by comparing with the known flow patterns in the database. Aftertrained by adjusting the system parameters using test samples, the experimental results show that theaccuracy of Bayes Classifier is 95.78% and that of NN Classifier is 98.71% in detecting P2P botnet virusesand suspected flows to achieve the goal of infection control in a short time.
