A Hybrid Ensemble Machine Learning Model for Cyber Threat Detection
Language: English
Published by Globeedit, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 117.03
Quantity: 1 available
Add to basketItem description from seller
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
- Author
- Emmanuel Ogala
- Publisher
- Globeedit
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 6630189620
- ISBN 13
- 9786630189629
- Item weight
- 340 grams
- Dimensions
- 220x150x13 mm
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| Item | 5 to 7 business days | 7 to 10 business days |
|---|---|---|
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