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A Hybrid Ensemble Machine Learning Model for Cyber Threat Detection
Print on DemandSeller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 216 pp. Englisch. Seller Inventory # 9786630189629
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A Hybrid Ensemble Machine Learning Model for Cyber Threat Detection
Print on DemandSeller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. A Hybrid Ensemble Machine Learning Model for Cyber Threat Detection | Emmanuel Ogala (u. a.) | Taschenbuch | Englisch | 2026 | GlobeEdit | EAN 9786630189629 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 135943637
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A Hybrid Ensemble Machine Learning Model for Cyber Threat Detection
Print on DemandSeller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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). 216 pp. Englisch. Seller Inventory # 9786630189629
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A Hybrid Ensemble Machine Learning Model for Cyber Threat Detection
Print on DemandSeller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. 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