Network Intrusion Detection in Software-Defined Network using Deep and Machine Learning

Software Defined Network (SDN) has been known for the great potential to become the development direction of a new generation network architecture. For the large-scale deployment of SDN in the future, security issues are a big concern. Accordingly, this paper applies several Machine Learning (ML)and deep learning (DL) models for Network Intrusion Detection System (NIDS), respectively, aiming at improving the accuracy performance on NIDS. The benchmark dataset, NSL-KDD, is used for evaluating the performance of the algorithms. Extensive experiments show that the F-measure rate can reach up to 87.72% for multiclass label on NSL-KDD data set with twenty-two features using KNN algorithm. Furthermore, compared to other ML models, the k-Nearest Neighbour model has better performance under multiclass classification through numerous experiments.