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

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.

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Abstract

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.

Item Type:	Proceeding Paper (Invited Papers)
Additional Information:	National collaboration: Dr Zainiharyati (UiTM) International collaboration: Eki Ahmad Dzaki Hamidi (UIN Sunan Gunung Djati)
Uncontrolled Keywords:	intrusion detection system, imbalanced classes, deep learning, feature extraction
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	09 Jan 2024 15:01
Last Modified:	15 Jan 2024 10:06
URI:	http://irep.iium.edu.my/id/eprint/109829

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