Abnormal Traffic Classification based on Feature Entropy Vector
Lulu Chen, Wenpu Guo, Hao He · 2017
Existing anomaly detection technology is mainly concerned with the detection of anomalous flow, and it is not enough to study anomaly type.Therefore, a method based on information entropy and k-means clustering is proposed to construct the anomalous traffic entropy feature vector to achieve fast and accurate judgment of anomaly types.The method is simple and easy to operate.Simulation results show that the proposed method is effective in classifying and determining the common types of network attack anomalies.