%0 Journal Article %1 noauthororeditor %A Huynh, Trong Thua %A Nguyen, Hoang Thanh %D 2022 %J International Journal of Computer Networks & Communications (IJCNC) %K IDS UNSW-NB15 deep learning network neural %N 01 %P 117-137 %R 10.5121/ijcnc.2022.14108 %T On The Performance of Intrusion Detection Systems with Hidden Multilayer Neural Network using DSD Training %U https://aircconline.com/ijcnc/V14N1/14122cnc08.pdf %V 14 %X 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.

@article{noauthororeditor, abstract = {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.}, added-at = {2022-03-23T06:13:17.000+0100}, author = {Huynh, Trong Thua and Nguyen, Hoang Thanh}, biburl = {https://www.bibsonomy.org/bibtex/2b2caefc9d8fc51d4097cf4c53720f181/laimbee}, doi = {10.5121/ijcnc.2022.14108}, interhash = {56ad6912e4a0a88af74c447f6bf03d91}, intrahash = {b2caefc9d8fc51d4097cf4c53720f181}, issn = {ISSN 0974 - 9322 (Online) ; 0975 - 2293 (Print)}, journal = {International Journal of Computer Networks & Communications (IJCNC)}, keywords = {IDS UNSW-NB15 deep learning network neural}, language = {English}, month = {January}, number = 01, pages = {117-137}, timestamp = {2022-03-23T06:13:17.000+0100}, title = {On The Performance of Intrusion Detection Systems with Hidden Multilayer Neural Network using DSD Training}, url = {https://aircconline.com/ijcnc/V14N1/14122cnc08.pdf}, volume = 14, year = 2022 }