IoT Network Intrusion Detection with Ensemble Learners

Sulyman Age Abdulkareem; Chuan Heng Foh; Haeyoung Lee; François Carrez; Klaus Moessner

doi:10.1109/ICTC55196.2022.9952376

IoT Network Intrusion Detection with Ensemble Learners

Sulyman Age Abdulkareem, Chuan Heng Foh, Haeyoung Lee, François Carrez, Klaus Moessner

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

Abstract

Protecting information systems against intruders' attacks requires utilising intrusion detection systems. Over the past several years, many open-source intrusion datasets have been made available so that academics and researchers can analyse and assess various detection classifiers' effectiveness. These datasets are made available with a full complement of illustrative network features. In this research, we investigate the issue of Network Intrusion Detection (NID) by utilising an Internet of Things (IoT) dataset called Bot-IoT to evaluate the detection efficiency and effectiveness of five different Ensemble Learning Classifiers (ELCs). Our experiment's results showed that despite all ELCs recording high classification metric scores, CatBoost emerged as the ELC that performed the best in our experiment in terms of Accuracy, Precision, F1-Score, Training and Test Time.

Original language	English
Title of host publication	2022 13th International Conference on Information and Communication Technology Convergence (ICTC)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	510-514
Number of pages	5
ISBN (Print)	978-1-6654-9940-8
DOIs	https://doi.org/10.1109/ICTC55196.2022.9952376
Publication status	Published - 21 Oct 2022
Event	2022 13th International Conference on Information and Communication Technology Convergence (ICTC): “Accelerating Digital Transformation with ICT Innovation” - Jeju Island, Korea, Democratic People's Republic of Duration: 19 Oct 2022 → 21 Oct 2022 Conference number: 13 https://ieeexplore.ieee.org/xpl/conhome/9952188/proceeding

Conference

Conference	2022 13th International Conference on Information and Communication Technology Convergence (ICTC)
Abbreviated title	ICTC 2022
Country/Territory	Korea, Democratic People's Republic of
City	Jeju Island
Period	19/10/22 → 21/10/22
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9952188/proceeding

Keywords

Training
Measurement
Network intrusion detection
Feature extraction
Recording
Information and communication technology
Internet of Things

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10.1109/ICTC55196.2022.9952376

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9952376

Cite this

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title = "IoT Network Intrusion Detection with Ensemble Learners",

abstract = "Protecting information systems against intruders' attacks requires utilising intrusion detection systems. Over the past several years, many open-source intrusion datasets have been made available so that academics and researchers can analyse and assess various detection classifiers' effectiveness. These datasets are made available with a full complement of illustrative network features. In this research, we investigate the issue of Network Intrusion Detection (NID) by utilising an Internet of Things (IoT) dataset called Bot-IoT to evaluate the detection efficiency and effectiveness of five different Ensemble Learning Classifiers (ELCs). Our experiment's results showed that despite all ELCs recording high classification metric scores, CatBoost emerged as the ELC that performed the best in our experiment in terms of Accuracy, Precision, F1-Score, Training and Test Time.",

keywords = "Training, Measurement, Network intrusion detection, Feature extraction, Recording, Information and communication technology, Internet of Things",

author = "Abdulkareem, {Sulyman Age} and Foh, {Chuan Heng} and Haeyoung Lee and Fran{\c c}ois Carrez and Klaus Moessner",

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doi = "10.1109/ICTC55196.2022.9952376",

language = "English",

isbn = "978-1-6654-9940-8",

pages = "510--514",

booktitle = "2022 13th International Conference on Information and Communication Technology Convergence (ICTC)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2022 13th International Conference on Information and Communication Technology Convergence (ICTC) : “Accelerating Digital Transformation with ICT Innovation”, ICTC 2022 ; Conference date: 19-10-2022 Through 21-10-2022",

url = "https://ieeexplore.ieee.org/xpl/conhome/9952188/proceeding",

}

Abdulkareem, SA, Foh, CH, Lee, H, Carrez, F & Moessner, K 2022, IoT Network Intrusion Detection with Ensemble Learners. in 2022 13th International Conference on Information and Communication Technology Convergence (ICTC)., 9952376, Institute of Electrical and Electronics Engineers (IEEE), pp. 510-514, 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, Democratic People's Republic of, 19/10/22. https://doi.org/10.1109/ICTC55196.2022.9952376

IoT Network Intrusion Detection with Ensemble Learners. / Abdulkareem, Sulyman Age; Foh, Chuan Heng; Lee, Haeyoung et al.
2022 13th International Conference on Information and Communication Technology Convergence (ICTC). Institute of Electrical and Electronics Engineers (IEEE), 2022. p. 510-514 9952376.

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

TY - GEN

T1 - IoT Network Intrusion Detection with Ensemble Learners

AU - Abdulkareem, Sulyman Age

AU - Foh, Chuan Heng

AU - Lee, Haeyoung

AU - Carrez, François

AU - Moessner, Klaus

N1 - Conference code: 13

PY - 2022/10/21

Y1 - 2022/10/21

N2 - Protecting information systems against intruders' attacks requires utilising intrusion detection systems. Over the past several years, many open-source intrusion datasets have been made available so that academics and researchers can analyse and assess various detection classifiers' effectiveness. These datasets are made available with a full complement of illustrative network features. In this research, we investigate the issue of Network Intrusion Detection (NID) by utilising an Internet of Things (IoT) dataset called Bot-IoT to evaluate the detection efficiency and effectiveness of five different Ensemble Learning Classifiers (ELCs). Our experiment's results showed that despite all ELCs recording high classification metric scores, CatBoost emerged as the ELC that performed the best in our experiment in terms of Accuracy, Precision, F1-Score, Training and Test Time.

AB - Protecting information systems against intruders' attacks requires utilising intrusion detection systems. Over the past several years, many open-source intrusion datasets have been made available so that academics and researchers can analyse and assess various detection classifiers' effectiveness. These datasets are made available with a full complement of illustrative network features. In this research, we investigate the issue of Network Intrusion Detection (NID) by utilising an Internet of Things (IoT) dataset called Bot-IoT to evaluate the detection efficiency and effectiveness of five different Ensemble Learning Classifiers (ELCs). Our experiment's results showed that despite all ELCs recording high classification metric scores, CatBoost emerged as the ELC that performed the best in our experiment in terms of Accuracy, Precision, F1-Score, Training and Test Time.

KW - Training

KW - Measurement

KW - Network intrusion detection

KW - Feature extraction

KW - Recording

KW - Information and communication technology

KW - Internet of Things

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U2 - 10.1109/ICTC55196.2022.9952376

DO - 10.1109/ICTC55196.2022.9952376

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EP - 514

BT - 2022 13th International Conference on Information and Communication Technology Convergence (ICTC)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2022 13th International Conference on Information and Communication Technology Convergence (ICTC)

Y2 - 19 October 2022 through 21 October 2022

ER -

IoT Network Intrusion Detection with Ensemble Learners

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Conference

Keywords

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