A Comparative Study of Deep Learning Models for Hyper Parameter Classification on UNSW-NB15

Seongsoo Kim; Lei Chen; Jongyeop Kim; Yiming Ji; Rami Haddad

doi:10.1109/SERA57763.2023.10197694

A Comparative Study of Deep Learning Models for Hyper Parameter Classification on UNSW-NB15

Research output: Contribution to book or proceeding › Conference article › peer-review

Abstract

Intrusion Detection System (IDS) is a crucial security mechanism for protecting computer networks from cyber-Attacks. Deep learning models have the potential to detect attack types by leveraging their ability to learn and extract features from large volumes of data. In this study, we compare the performance of four different deep learning algorithms for IDS: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), bidirectional LSTM, and bidirectional GRU. We evaluate the attack prediction accuracy for three types of attacks: Denial of Service (DoS), Generic, and Exploits. We vary each algorithm's range parameter and epochs and determine the best parameter combination sets for achieving the highest accuracy. Our experimental results demonstrate that increased range parameters influence the accuracy of LSTM, bi-LSTM, and Bi-GRU models. Ultimately, GRU proved to have the most outstanding performance among the four algorithms tested.

Original language	English
Title of host publication	Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023
Editors	Yeong-Tae Song, Junghwan Rhee, Yuseok Jeon
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	218-228
Number of pages	11
ISBN (Electronic)	9798350345889
ISBN (Print)	9798350345889
DOIs	https://doi.org/10.1109/SERA57763.2023.10197694
State	Published - May 23 2023
Event	21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023 - Orlando, United States Duration: May 23 2023 → May 25 2023

Publication series

Name	2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)

Conference

Conference	21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023
Country/Territory	United States
City	Orlando
Period	05/23/23 → 05/25/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Scopus Subject Areas

Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Industrial and Manufacturing Engineering
Safety, Risk, Reliability and Quality
Environmental Engineering

Keywords

Bidirectional GRU
Bidirectional LSTM
GRU
IDS
Intrusion Detection
LSTM
UNSW-NB15
deep learning

Access to Document

10.1109/SERA57763.2023.10197694

Cite this

Kim, S., Chen, L., Kim, J., Ji, Y., & Haddad, R. (2023). A Comparative Study of Deep Learning Models for Hyper Parameter Classification on UNSW-NB15. In Y.-T. Song, J. Rhee, & Y. Jeon (Eds.), Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023 (pp. 218-228). (2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SERA57763.2023.10197694

Kim, Seongsoo ; Chen, Lei ; Kim, Jongyeop et al. / A Comparative Study of Deep Learning Models for Hyper Parameter Classification on UNSW-NB15. Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023. editor / Yeong-Tae Song ; Junghwan Rhee ; Yuseok Jeon. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 218-228 (2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)).

@inproceedings{19f00567726b491a88dfb0aae5eaa1ea,

title = "A Comparative Study of Deep Learning Models for Hyper Parameter Classification on UNSW-NB15",

abstract = "Intrusion Detection System (IDS) is a crucial security mechanism for protecting computer networks from cyber-Attacks. Deep learning models have the potential to detect attack types by leveraging their ability to learn and extract features from large volumes of data. In this study, we compare the performance of four different deep learning algorithms for IDS: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), bidirectional LSTM, and bidirectional GRU. We evaluate the attack prediction accuracy for three types of attacks: Denial of Service (DoS), Generic, and Exploits. We vary each algorithm's range parameter and epochs and determine the best parameter combination sets for achieving the highest accuracy. Our experimental results demonstrate that increased range parameters influence the accuracy of LSTM, bi-LSTM, and Bi-GRU models. Ultimately, GRU proved to have the most outstanding performance among the four algorithms tested.",

keywords = "Bidirectional GRU, Bidirectional LSTM, GRU, IDS, Intrusion Detection, LSTM, UNSW-NB15, deep learning",

author = "Seongsoo Kim and Lei Chen and Jongyeop Kim and Yiming Ji and Rami Haddad",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023 ; Conference date: 23-05-2023 Through 25-05-2023",

year = "2023",

month = may,

day = "23",

doi = "10.1109/SERA57763.2023.10197694",

language = "English",

isbn = "9798350345889",

series = "2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "218--228",

editor = "Yeong-Tae Song and Junghwan Rhee and Yuseok Jeon",

booktitle = "Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023",

address = "United States",

}

Kim, S, Chen, L , Kim, J , Ji, Y & Haddad, R 2023, A Comparative Study of Deep Learning Models for Hyper Parameter Classification on UNSW-NB15. in Y-T Song, J Rhee & Y Jeon (eds), Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023. 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA), Institute of Electrical and Electronics Engineers Inc., pp. 218-228, 21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023, Orlando, United States, 05/23/23. https://doi.org/10.1109/SERA57763.2023.10197694

A Comparative Study of Deep Learning Models for Hyper Parameter Classification on UNSW-NB15. / Kim, Seongsoo; Chen, Lei ; Kim, Jongyeop et al.
Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023. ed. / Yeong-Tae Song; Junghwan Rhee; Yuseok Jeon. Institute of Electrical and Electronics Engineers Inc., 2023. p. 218-228 (2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

T1 - A Comparative Study of Deep Learning Models for Hyper Parameter Classification on UNSW-NB15

AU - Kim, Seongsoo

AU - Chen, Lei

AU - Kim, Jongyeop

AU - Ji, Yiming

AU - Haddad, Rami

PY - 2023/5/23

Y1 - 2023/5/23

N2 - Intrusion Detection System (IDS) is a crucial security mechanism for protecting computer networks from cyber-Attacks. Deep learning models have the potential to detect attack types by leveraging their ability to learn and extract features from large volumes of data. In this study, we compare the performance of four different deep learning algorithms for IDS: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), bidirectional LSTM, and bidirectional GRU. We evaluate the attack prediction accuracy for three types of attacks: Denial of Service (DoS), Generic, and Exploits. We vary each algorithm's range parameter and epochs and determine the best parameter combination sets for achieving the highest accuracy. Our experimental results demonstrate that increased range parameters influence the accuracy of LSTM, bi-LSTM, and Bi-GRU models. Ultimately, GRU proved to have the most outstanding performance among the four algorithms tested.

AB - Intrusion Detection System (IDS) is a crucial security mechanism for protecting computer networks from cyber-Attacks. Deep learning models have the potential to detect attack types by leveraging their ability to learn and extract features from large volumes of data. In this study, we compare the performance of four different deep learning algorithms for IDS: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), bidirectional LSTM, and bidirectional GRU. We evaluate the attack prediction accuracy for three types of attacks: Denial of Service (DoS), Generic, and Exploits. We vary each algorithm's range parameter and epochs and determine the best parameter combination sets for achieving the highest accuracy. Our experimental results demonstrate that increased range parameters influence the accuracy of LSTM, bi-LSTM, and Bi-GRU models. Ultimately, GRU proved to have the most outstanding performance among the four algorithms tested.

KW - Bidirectional GRU

KW - Bidirectional LSTM

KW - GRU

KW - IDS

KW - Intrusion Detection

KW - LSTM

KW - UNSW-NB15

KW - deep learning

UR - https://www.scopus.com/pages/publications/85168760243

U2 - 10.1109/SERA57763.2023.10197694

DO - 10.1109/SERA57763.2023.10197694

M3 - Conference article

AN - SCOPUS:85168760243

SN - 9798350345889

T3 - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)

SP - 218

EP - 228

BT - Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023

A2 - Song, Yeong-Tae

A2 - Rhee, Junghwan

A2 - Jeon, Yuseok

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023

Y2 - 23 May 2023 through 25 May 2023

ER -

Kim S, Chen L , Kim J , Ji Y , Haddad R. A Comparative Study of Deep Learning Models for Hyper Parameter Classification on UNSW-NB15. In Song YT, Rhee J, Jeon Y, editors, Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 218-228. (2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)). doi: 10.1109/SERA57763.2023.10197694

A Comparative Study of Deep Learning Models for Hyper Parameter Classification on UNSW-NB15

Abstract

Publication series

Conference

UN SDGs

Scopus Subject Areas

Keywords

Access to Document

Other files and links

Fingerprint

Cite this