Network Anomaly Traffic Detection Method Based on Support Vector Machine
Network traffic is the amount of data moving across a network at a specific time, and anomaly traffic detection is of great importance in computer networks. In this paper, we integrate the entropy theory and support vector machine to detect Network anomaly traffic. We utilize the entropy theory to establish the network traffic feature vector using the entropy theory, and then exploit the support vector machine to detect the network anomaly traffic by tackling a classification problem. Particularly, six types of network features are used to construct feature vector in this work, such as Source IP, Destination IP, Source Port, Destination Port, Packet Size and Packet Type. Afterwards, we provide the network feature vector to SVM to learn network traffic behaviors. Experimental results demonstrate that compared with existing method, our proposed method can detect network anomaly traffic with high accuracy.
2016 International Conference on Smart City and Systems Engineering (ICSCSE)