Intrusion Prediction using LSTM and GRU with UNSW-NB15
Seong-Soo Kim, Lei Chen, Jongyeop Kim · 2021
This study proposes a deep learning model for predicting the timing of attacks by applying Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) to an intrusion detection system (IDS) dataset UNSW-NB15. We applied the finite state machine concept to convert floating-point values to equivalent binary ones to increase model accuracy. As a result, the accuracy of the GRU and LSTM by average 13% and 18% respectively in terms of weighted F1 score.