Network Anomaly Detection and Identification Based on Deep Learning Methods

Network anomaly detection is the process of determining when network behavior has deviated from the normal behavior. The detection of abnormal events in large dynamic network has become increasingly important as networks grow in size and complexity. However, fast and accurate network anomaly detection is very challenging. Deep learning is a potential method for network anomaly detection due to its good feature modeling capability. This paper presents a new anomaly detection method based on deep learning models, specifically the feedforward neural network (FNN) model and convolutional neural network (CNN) model. The performance of the models is evaluated by several experiments with a popular NSL-KDD dataset. From the experimental results, we find the FNN and CNN models not only have a strong modeling ability for network anomaly detection, but also have high accuracy. Compared with several traditional machine learning methods, such as J48, Naive Bayes, NB Tree, Random Forest, Random Tree and SVM, the proposed models obtain a higher accuracy and detection rate with lower false positive rate. The deep learning models can effectively improve both the detection accuracy and the ability to identify anomaly types.