STUDYING THE IMPACT OF DATASET BALANCING ON MACHINE LEARNING-BASED INTRUSION DETECTION SYSTEMS FOR IOT

Abdel-Hamid, Salma; Hegazy, Islam; Aref, Mostafa; Roushdy, Mohamed

doi:10.21608/ijicis.2024.317982.1352

	STUDYING THE IMPACT OF DATASET BALANCING ON MACHINE LEARNING-BASED INTRUSION DETECTION SYSTEMS FOR IOT
International Journal of Intelligent Computing and Information Sciences
Volume 24, Issue 3, September 2024, Page 41-57 PDF (1.11 MB)
Document Type: Original Article
DOI: 10.21608/ijicis.2024.317982.1352
View on SCiNiTO
Authors
Salma Abdel-Hamid ¹; Islam Hegazy ²; Mostafa Aref ³; Mohamed Roushdy ⁴
¹Computer Science Department Faculty of Computers and Information Technology Future University in Egypt
²Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University
³Department Computer Science, Faculty of Computer and Information Sciences,Ain Shams University, Cairo, Egypt.
⁴Faculty of Computer Science and Information Technology Innovation University, Sharqia, Egypt
Abstract
Internet of Things (IoT) networks are integral to modern life due to their pervasive connectivity and automation capabilities. Intrusion Detection Systems (IDS) are crucial in IoT ecosystems to countermeasure attacks that can compromise devices and disrupt essential services. Their role is vital in maintaining the integrity, confidentiality, and availability of data within these networks. The effectiveness of these security systems is fundamentally dependent on the robustness of learning algorithms and the quality of the datasets utilized. Class imbalance is a common challenge in real-world datasets, where certain classes are represented by significantly fewer instances compared to others. This paper studies the impact of balancing the BoT-IoT dataset on the performance of Machine Learning (ML) based IDSs using three algorithms: K-Nearest Neighbors (KNN), Gradient Boosting (GB), and Support Vector Machine (SVM). To address the class imbalance problem, we apply two resampling techniques, random upsampling and Synthetic Minority Over-sampling Technique (SMOTE). We evaluate the efficacy of the models through various performance metrics, including accuracy, precision, recall, and F1-score. The findings of our experimental work prove that balanced datasets lead to more dependable and robust IDSs that are capable of handling real-world data with varied class distributions.
Keywords
Internet of Things; Intrusion Detection; Machine Learning; SMOTE


Statistics Article View: 147 PDF Download: 151