Open AccessOn The Performance of Intrusion Detection Systems with Hidden Multilayer Neural Network using DSD Training
Author(s) -
Trong Thua Huynh,
Hoang Nguyen
Publication year - 2022
Publication title -
international journal of computer networks and communications
Language(s) - English
Resource type - Journals
eISSN - 0975-2293
pISSN - 0974-9322
DOI - 10.5121/ijcnc.2022.14108
Subject(s) - computer science , intrusion detection system , artificial neural network , recurrent neural network , artificial intelligence , constant false alarm rate , machine learning , long short term memory , deep learning , false alarm , intrusion , alarm , data mining , pattern recognition (psychology) , geochemistry , geology , materials science , composite material
Deep learning applications, especially multilayer neural network models, result in network intrusion detection with high accuracy. This study proposes a model that combines a multilayer neural network with Dense Sparse Dense (DSD) multi-stage training to simultaneously improve the criteria related to the performance of intrusion detection systems on a comprehensive dataset UNSW-NB15. We conduct experiments on many neural network models such as Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), etc. to evaluate the combined efficiency with each model through many criteria such as accuracy, detection rate, false alarm rate, precision, and F1-Score.