Impact of dataset balancing on machine learning-based intrusion detection systems

Yusri, Muhammad Iqbal and Habaebi, Mohamed Hadi and Gunawan, Teddy Surya and Mansor, Hasmah and Kartiwi, Mira and Nur, Levy Olivia (2024) Impact of dataset balancing on machine learning-based intrusion detection systems. In: 2024 IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 30-31 July 2024, BANDUNG, INDONESIA.

PDF - Published Version Restricted to Registered users only Download (1MB) | Request a copy

Abstract

Intrusion Detection Systems (IDS) are indispensable for cybersecurity, as they safeguard networks from increasingly sophisticated and sophisticated cyberattacks. This paper assesses the influence of dataset balancing on the performance of machine learning-based IDS, thereby addressing the challenge of imbalanced data in detecting network intrusions. We concentrate on three IDS implementations: Tree-based Intelligent IDS, Multi-Tiered Hybrid IDS (MTH-IDS), and Leader Class and Confidence Decision Ensemble (LCCDE). We utilized the Synthetic Minority Over-Sampling Technique (SMOTE) to balance data and implemented feature selection and hyperparameter optimization to improve the model's performance using the CICIDS 2017 dataset. The LCCDE model exhibits the highest performance, as our comparative analysis demonstrates that the combination of SMOTE and feature selection enhances the F1 scores. The results underscore the significance of advanced ensemble techniques and data preprocessing in developing resilient IDS. This research emphasizes the necessity of ongoing optimization and evaluation of IDS models to guarantee effective protection against the development of cyber threats.

Item Type:	Proceeding Paper (Invited Papers)
Uncontrolled Keywords:	Intrusion detection system, machine learning, tree-based intelligence, MTH IDS, LCCDE.
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 > Department of Electrical and Computer Engineering Kulliyyah of Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	20 Sep 2024 15:45
Last Modified:	20 Sep 2024 15:45
URI:	http://irep.iium.edu.my/id/eprint/114534

Actions (login required)

View Item