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Deep learning-based high performance intrusion detection system for imbalanced datasets

In recent years, the explosive growth in internet and technology use has led to an alarming escalation in both the frequency and severity of cyberattacks. As such, proactive detection and prevention of these attacks have become a matter of paramount importance. This need for vigilance has catalyzed...

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Main Authors: Assaig, Faisal Ahmed, Gunawan, Teddy Surya, Nordin, Anis Nurashikin, Ab. Rahim, Rosminazuin, Mohd Zain, Zainihariyati, Hamidi, Eki Ahmad Zaki
Format: Proceeding Paper
Language:English
English
Published: IEEE 2023
Subjects:
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/109829/7/109829_Deep%20learning-based%20high%20performance%20intrusion%20detection.pdf
http://irep.iium.edu.my/109829/13/109829_Deep%20learning-based%20high%20performance%20intrusion%20detection_SCOPUS.pdf
http://irep.iium.edu.my/109829/
https://ieeexplore.ieee.org/document/10335377
https://doi.org/10.1109/ICWT58823.2023.10335377
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spelling my.iium.irep.1098292024-01-15T02:06:04Z http://irep.iium.edu.my/109829/ Deep learning-based high performance intrusion detection system for imbalanced datasets Assaig, Faisal Ahmed Gunawan, Teddy Surya Nordin, Anis Nurashikin Ab. Rahim, Rosminazuin Mohd Zain, Zainihariyati Hamidi, Eki Ahmad Zaki TK7885 Computer engineering In recent years, the explosive growth in internet and technology use has led to an alarming escalation in both the frequency and severity of cyberattacks. As such, proactive detection and prevention of these attacks have become a matter of paramount importance. This need for vigilance has catalyzed the adoption of Machine Learning (ML) and Deep Learning (DL) techniques to effectively identify and analyze network traffic content, predict potential cyberattacks, and respond promptly to these security threats. ML and DL methods offer innovative solutions by facilitating the development of sophisticated models that meticulously analyze patterns in network traffic data. By identifying deviations from expected behaviors, these techniques enable the early detection and prevention of impending attacks. They achieve this by learning from the data, improving their ability to detect attacks over time, and responding effectively to new, unseen threats. However, contemporary intrusion detection methods face significant challenges, particularly related to imbalanced classes, low detection rates, and high false alarm rates. Addressing these hurdles is critical for the development of a robust and efficient intrusion detection system. Our research seeks to confront these issues head-on, by proposing an innovative, high-performance intrusion detection system tailored specifically to handle imbalanced datasets. Our methodology not only offers improvements in detection rates and false alarm rates but also provides a feasible solution for handling class imbalance in the data. We anticipate that our findings will pave the way for more robust cyber defense mechanisms in this era of ever-evolving security threats. IEEE 2023-12-11 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/109829/7/109829_Deep%20learning-based%20high%20performance%20intrusion%20detection.pdf application/pdf en http://irep.iium.edu.my/109829/13/109829_Deep%20learning-based%20high%20performance%20intrusion%20detection_SCOPUS.pdf Assaig, Faisal Ahmed and Gunawan, Teddy Surya and Nordin, Anis Nurashikin and Ab. Rahim, Rosminazuin and Mohd Zain, Zainihariyati and Hamidi, Eki Ahmad Zaki (2023) Deep learning-based high performance intrusion detection system for imbalanced datasets. In: 2023 9th International Conference on Wireless and Telematics (ICWT), 6-7 July 2023, Solo, Indonesia. https://ieeexplore.ieee.org/document/10335377 https://doi.org/10.1109/ICWT58823.2023.10335377
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic TK7885 Computer engineering
spellingShingle TK7885 Computer engineering
Assaig, Faisal Ahmed
Gunawan, Teddy Surya
Nordin, Anis Nurashikin
Ab. Rahim, Rosminazuin
Mohd Zain, Zainihariyati
Hamidi, Eki Ahmad Zaki
Deep learning-based high performance intrusion detection system for imbalanced datasets
description In recent years, the explosive growth in internet and technology use has led to an alarming escalation in both the frequency and severity of cyberattacks. As such, proactive detection and prevention of these attacks have become a matter of paramount importance. This need for vigilance has catalyzed the adoption of Machine Learning (ML) and Deep Learning (DL) techniques to effectively identify and analyze network traffic content, predict potential cyberattacks, and respond promptly to these security threats. ML and DL methods offer innovative solutions by facilitating the development of sophisticated models that meticulously analyze patterns in network traffic data. By identifying deviations from expected behaviors, these techniques enable the early detection and prevention of impending attacks. They achieve this by learning from the data, improving their ability to detect attacks over time, and responding effectively to new, unseen threats. However, contemporary intrusion detection methods face significant challenges, particularly related to imbalanced classes, low detection rates, and high false alarm rates. Addressing these hurdles is critical for the development of a robust and efficient intrusion detection system. Our research seeks to confront these issues head-on, by proposing an innovative, high-performance intrusion detection system tailored specifically to handle imbalanced datasets. Our methodology not only offers improvements in detection rates and false alarm rates but also provides a feasible solution for handling class imbalance in the data. We anticipate that our findings will pave the way for more robust cyber defense mechanisms in this era of ever-evolving security threats.
format Proceeding Paper
author Assaig, Faisal Ahmed
Gunawan, Teddy Surya
Nordin, Anis Nurashikin
Ab. Rahim, Rosminazuin
Mohd Zain, Zainihariyati
Hamidi, Eki Ahmad Zaki
author_facet Assaig, Faisal Ahmed
Gunawan, Teddy Surya
Nordin, Anis Nurashikin
Ab. Rahim, Rosminazuin
Mohd Zain, Zainihariyati
Hamidi, Eki Ahmad Zaki
author_sort Assaig, Faisal Ahmed
title Deep learning-based high performance intrusion detection system for imbalanced datasets
title_short Deep learning-based high performance intrusion detection system for imbalanced datasets
title_full Deep learning-based high performance intrusion detection system for imbalanced datasets
title_fullStr Deep learning-based high performance intrusion detection system for imbalanced datasets
title_full_unstemmed Deep learning-based high performance intrusion detection system for imbalanced datasets
title_sort deep learning-based high performance intrusion detection system for imbalanced datasets
publisher IEEE
publishDate 2023
url http://irep.iium.edu.my/109829/7/109829_Deep%20learning-based%20high%20performance%20intrusion%20detection.pdf
http://irep.iium.edu.my/109829/13/109829_Deep%20learning-based%20high%20performance%20intrusion%20detection_SCOPUS.pdf
http://irep.iium.edu.my/109829/
https://ieeexplore.ieee.org/document/10335377
https://doi.org/10.1109/ICWT58823.2023.10335377
_version_ 1789424037009555456
score 13.211869

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