A Network Traffic anomaly Detection method based on CNN and XGBoost

With the rapid development of information network, network security is becoming more and more important. Intrusion detection is an important component of the network security system. The traditional signature-based matching detection method is difficult to cope with the increasingly complex network environment. On the contrary, anomaly detection which is based on network traffic pattern analysis has obvious advantages in dealing with encryption attacks, zero-day attacks and other new attacks. This paper studies the network traffic anomaly detection, and proposes a traffic anomaly detection model which combines convolution neural network and eXtreme Gradient Boosting algorithm. First, the collected traffic data is preprocessed into appropriate format that meets the input requirements of the model. Then the improved LeNet-5 convolution neural network is used for feature learning, and finally XGBoost algorithm is used to classify the learning features. Experimental results show that the proposed network traffic anomaly detection method based on CNN and XGBoost has a high accuracy, and good experimental results have been achieved in both two- classification and multi-classification.