Method of intrusion detection using deep neural network

Abstract

In this study, an artificial intelligence (AI) intrusion detection system using a deep neural network (DNN) was investigated and tested with the KDD Cup 99 dataset in response to ever-evolving network attacks. First, the data were preprocessed through data transformation and normalization for input to the DNN model. The DNN algorithm was applied to the data refined through preprocessing to create a learning model, and the entire KDD Cup 99 dataset was used to verify it. Finally, the accuracy, detection rate, and false alarm rate were calculated to ascertain the detection efficacy of the DNN model, which was found to generate good results for intrusion detection.