Constructing an Intrusion Detection Model based on Long Short-term Neural Networks

In this paper an intrusion detection model is constructed based on the long short-term neural networks. Training of the model and tests on its performance are done with KDD 99 and UNSW-NB15 data-sets by choosing the optimum parameters via experiments. What’s more, a comparison is done with the traditional machine learning model. The results show that this intrusion detection model has a higher detection accuracy than the traditional machine learning models. The detection accuracy with respect to the two data-sets is 98.99% and 99.41% respectively.