Network anomaly detection based on ensemble learning

With the improvement of facility condition and network speed, the network traffic has also presented an exponential growth in recent years. However, a growing number of problems concerning cyber security have appeared. Anomaly network traffic detection can identify abnormal traffic from massive traffic. Accurate identification can reduce anomaly network traffic and protect user client. Our research is based on the traffic collected and processed by National Chiao Tung University. These data include normal traffic and abnormal traffic, the former one being majority. We propose a processing method with high accuracy. We first pre-sampled the data set, and then analyzed the data features. Later on, based on theoretical research, we integrated the model of random forests and other boosting method and proposed a greedy multi-classification to binary classification model based on ensemble learning model.