AIRCC's International Journal of Computer Science and Information Technology

Wernhuar Tarng ¹, Cheng-Kang Chou ¹, Kuo-Liang Ou ¹

Affiliations
1 Graduate Institute of Computer Science, National Hsinchu University of Education, TW

Abstract

A P2P botnet virus detection system based on data-mining algorithms is proposed in this study to detect the infected computers quickly using Bayes Classifier 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 results show that the accuracy of Bayes Classifier is 95.78% and that of NN Classifier is 98.71% in detecting P2P botnet viruses and suspected flows to achieve the goal of infection control in a short time.

Keywords

Data Mining, Bayes Classifier, Neural Network, P2P Botnet, Virus Detection Systems.

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Wernhuar Tarng ¹, Hao-Wei Lin ¹, Kuo-Liang Ou ¹

Affiliations
1 Graduate Institute of Computer Science, National Hsinchu University of Education, Hsinchu, TW

Abstract

The electricity of sensor nodes in wireless sensor networks is very limited, so it is an important research topic to deploy the sensor nodes and cooperate with an efficient routing algorithm for extending the network lifetime. In the related research, LEACH routing algorithm randomly selects cluster heads in each round to form a cluster network, which may cause additional power consumption and inability to maintain the optimal routes for data transmission. The cluster allocation and routing algorithm proposed in this study is based on the cluster architecture of LEACH, and the objective is to produce clusters with more sensor nodes to balance energy consumption of cluster head. For indirect-transmission routing algorithms, the sensor nodes near the base station may consume more energy due to a larger amount of data transmission. Therefore, this study proposed to increase the node density near the base station during deployment to compensate for the requirement of high energy consumption. The experimental results show that the proposed algorithm based on node density distribution can efficiently increase the lifetime of wireless sensor networks.

Keywords

Wireless Sensor Networks, Routing Algorithms, Cluster Networks, Node Density, Network Lifetime.

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