Deep feature selection for machine learning based attack detection systems

Abstract

The typical intrusion detection system (IDS) based on machine learning classifies normal and attack network traffic by extracting and analyzing network features. However, several extracted features are irrelevant and may degrade the classification accuracy. In addition, they also increase the training time and model size. Therefore, feature selection is an essential process in building an IDS system. In this paper, we propose a feature selection method for IDS by employing a Deep Neural Network model to search for and select the most crucial features. The proposal is evaluated with two datasets UNSW-NB15 and CIC-IDS2017, and archives superior results compared with other feature selection algorithms with accuracy up to 99.96% for UNSW-NB15, 99.88% for CIC-IDS2017 while combining with LSTM-based IDS. It also reduces significant data size and time for training.