Modeling Network Intrusion Detection System Using Feed-Forward Neural Network Using UNSW-NB15 Dataset

Ordinary machine learning algorithms are not very efficient in solving the classification problem of Network Intrusion because of the huge amount of data. Deep Learning is proven to be more effective in this scenario. Deep Learning can effectively classify with high dimensionality and complex features. In this paper, a deep learning IDS is proposed using state of the art UNSW-NB15 dataset. An experiment conducted to select the optimal activation function and features and then testing on unseen data demonstrates high accuracy and lower false alarm rate. The evaluation results show that proposed classifier outperforms other machine learning models, thus opening new dimensions in research in Network Intrusion Detection.