Data Balanced Algorithm Based on Generative Adversarial Network

I-Hsien Liu; Cheng En Hsieh; Wei Min Lin; Jung Shian Li; Chu Fen Li

Data Balanced Algorithm Based on Generative Adversarial Network

I-Hsien Liu, Cheng En Hsieh, Wei Min Lin, Jung Shian Li, Chu Fen Li

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In order to defend against malicious attacks, intrusion detection systems have introduced machine learning as a protection strategy. However, machine learning algorithms and datasets have a great influence on the effectiveness of the machine learning model. This study uses five algorithms which are Naïve Bayes, CNN, LSTM, BAT, and SVM to train the IDS machine learning model. We design a data-balanced method based on the GAN algorithm to improve the data imbalance problem of the IDS dataset.

Original language	English
Title of host publication	Proceedings of the International Conference on Artificial Life and Robotics, ICAROB 2022
Editors	Yingmin Jia, Takao Ito, Ju-Jang Lee
Publisher	ALife Robotics Corporation Ltd
Pages	645-649
Number of pages	5
ISBN (Print)	9784990835071
Publication status	Published - 2022
Event	27th International Conference on Artificial Life and Robotics, ICAROB 2022 - Virtual, Online Duration: 2022 Jan 20 → 2022 Jan 23

Publication series

Name	Proceedings of International Conference on Artificial Life and Robotics
ISSN (Electronic)	2435-9157

Conference

Conference	27th International Conference on Artificial Life and Robotics, ICAROB 2022
City	Virtual, Online
Period	22-01-20 → 22-01-23

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Vision and Pattern Recognition
Hardware and Architecture
Information Systems
Control and Systems Engineering
Electrical and Electronic Engineering
Modelling and Simulation

Cite this

Liu, I.-H., Hsieh, C. E., Lin, W. M., Li, J. S., & Li, C. F. (2022). Data Balanced Algorithm Based on Generative Adversarial Network. In Y. Jia, T. Ito, & J.-J. Lee (Eds.), Proceedings of the International Conference on Artificial Life and Robotics, ICAROB 2022 (pp. 645-649). (Proceedings of International Conference on Artificial Life and Robotics). ALife Robotics Corporation Ltd.

Liu, I-Hsien ; Hsieh, Cheng En ; Lin, Wei Min et al. / Data Balanced Algorithm Based on Generative Adversarial Network. Proceedings of the International Conference on Artificial Life and Robotics, ICAROB 2022. editor / Yingmin Jia ; Takao Ito ; Ju-Jang Lee. ALife Robotics Corporation Ltd, 2022. pp. 645-649 (Proceedings of International Conference on Artificial Life and Robotics).

@inproceedings{62df32e710f14fca998c35c3a9ddcf4f,

title = "Data Balanced Algorithm Based on Generative Adversarial Network",

abstract = "In order to defend against malicious attacks, intrusion detection systems have introduced machine learning as a protection strategy. However, machine learning algorithms and datasets have a great influence on the effectiveness of the machine learning model. This study uses five algorithms which are Na{\"i}ve Bayes, CNN, LSTM, BAT, and SVM to train the IDS machine learning model. We design a data-balanced method based on the GAN algorithm to improve the data imbalance problem of the IDS dataset.",

author = "I-Hsien Liu and Hsieh, {Cheng En} and Lin, {Wei Min} and Li, {Jung Shian} and Li, {Chu Fen}",

note = "Funding Information: This work was supported by the Ministry of Science and Technology (MOST) in Taiwan under contract numbers MOST 108-2221-E-006-110-MY3 and MOST 110-2218-E-006-013-MBK. Publisher Copyright: {\textcopyright} The 2022 International Conference on Artificial Life and Robotics (ICAROB2022).; 27th International Conference on Artificial Life and Robotics, ICAROB 2022 ; Conference date: 20-01-2022 Through 23-01-2022",

year = "2022",

language = "English",

isbn = "9784990835071",

series = "Proceedings of International Conference on Artificial Life and Robotics",

publisher = "ALife Robotics Corporation Ltd",

pages = "645--649",

editor = "Yingmin Jia and Takao Ito and Ju-Jang Lee",

booktitle = "Proceedings of the International Conference on Artificial Life and Robotics, ICAROB 2022",

}

Liu, I-H, Hsieh, CE, Lin, WM, Li, JS & Li, CF 2022, Data Balanced Algorithm Based on Generative Adversarial Network. in Y Jia, T Ito & J-J Lee (eds), Proceedings of the International Conference on Artificial Life and Robotics, ICAROB 2022. Proceedings of International Conference on Artificial Life and Robotics, ALife Robotics Corporation Ltd, pp. 645-649, 27th International Conference on Artificial Life and Robotics, ICAROB 2022, Virtual, Online, 22-01-20.

Data Balanced Algorithm Based on Generative Adversarial Network. / Liu, I-Hsien; Hsieh, Cheng En; Lin, Wei Min et al.
Proceedings of the International Conference on Artificial Life and Robotics, ICAROB 2022. ed. / Yingmin Jia; Takao Ito; Ju-Jang Lee. ALife Robotics Corporation Ltd, 2022. p. 645-649 (Proceedings of International Conference on Artificial Life and Robotics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Data Balanced Algorithm Based on Generative Adversarial Network

AU - Liu, I-Hsien

AU - Hsieh, Cheng En

AU - Lin, Wei Min

AU - Li, Jung Shian

AU - Li, Chu Fen

N1 - Funding Information: This work was supported by the Ministry of Science and Technology (MOST) in Taiwan under contract numbers MOST 108-2221-E-006-110-MY3 and MOST 110-2218-E-006-013-MBK. Publisher Copyright: © The 2022 International Conference on Artificial Life and Robotics (ICAROB2022).

PY - 2022

Y1 - 2022

N2 - In order to defend against malicious attacks, intrusion detection systems have introduced machine learning as a protection strategy. However, machine learning algorithms and datasets have a great influence on the effectiveness of the machine learning model. This study uses five algorithms which are Naïve Bayes, CNN, LSTM, BAT, and SVM to train the IDS machine learning model. We design a data-balanced method based on the GAN algorithm to improve the data imbalance problem of the IDS dataset.

AB - In order to defend against malicious attacks, intrusion detection systems have introduced machine learning as a protection strategy. However, machine learning algorithms and datasets have a great influence on the effectiveness of the machine learning model. This study uses five algorithms which are Naïve Bayes, CNN, LSTM, BAT, and SVM to train the IDS machine learning model. We design a data-balanced method based on the GAN algorithm to improve the data imbalance problem of the IDS dataset.

UR - http://www.scopus.com/inward/record.url?scp=85125135315&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85125135315&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85125135315

SN - 9784990835071

T3 - Proceedings of International Conference on Artificial Life and Robotics

SP - 645

EP - 649

BT - Proceedings of the International Conference on Artificial Life and Robotics, ICAROB 2022

A2 - Jia, Yingmin

A2 - Ito, Takao

A2 - Lee, Ju-Jang

PB - ALife Robotics Corporation Ltd

T2 - 27th International Conference on Artificial Life and Robotics, ICAROB 2022

Y2 - 20 January 2022 through 23 January 2022

ER -

Data Balanced Algorithm Based on Generative Adversarial Network

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Other files and links

Fingerprint

Cite this