A Hybrid Ensemble Machine Learning Model for Cyber Threat Detection

Emmanuel Ogala

ISBN 10: 6630189620 / ISBN 13: 9786630189629

Published by Globeedit, 2026

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - The rapid growth of digital technologies and internet-based services has significantly increased cybersecurity risks, necessitating more effective and intelligent threat detection mechanisms. Traditional cybersecurity approaches, including signature-based and behavior-based detection techniques, often struggle to cope with the increasing complexity and diversity of modern cyberattacks. To address these limitations, this study proposes a hybrid machine learning framework for cyber threat detection and classification that combines the strengths of multiple learning algorithms through a weighted voting strategy. The proposed framework integrates six machine learning classifiers: K-Nearest Neighbors (KNN), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Light Gradient Boosting (LGB). An empirical evaluation was conducted to assess their performance in both binary and multiclass classification tasks. For binary classification, the individual models achieved accuracies of 91.78% (KNN), 94.29% (RF), 82.18% (NB), 90.70% (SVM), 87.39% (LDA), and 94.24% (LGB).

Seller Inventory # 9786630189629

Bibliographic Details

• Title: A Hybrid Ensemble Machine Learning Model for Cyber Threat Detection
• Publisher: Globeedit
• Publication Date: 2026
• Language: English
• ISBN 10: 6630189620
• ISBN 13: 9786630189629
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 13D millimeters 220 millimeters width by 150 millimeters height by 13 millimeters depth
• Weight: 340 grams