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Record Nr. |
UNISA996464436503316 |
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Titolo |
Machine learning and data mining for emerging trend in cyber dynamics : theories and applications / / edited by Haruna Chiroma, 3 others |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2021] |
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©2021 |
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ISBN |
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Descrizione fisica |
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1 online resource (315 pages) : illustrations |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Intro -- Contents -- A Survey of Machine Learning for Network Fault Management -- 1 Introduction -- 2 Network Fault Management -- 3 Pattern Mining-Based Approaches -- 3.1 Episode and Association Rules Mining-Based Approaches -- 3.2 Sequential Pattern Mining-Based Approaches -- 3.3 Clustering-Based Approaches -- 3.4 Summary and Perspective -- 4 Machine Learning-Based Approaches -- 4.1 Artificial Neural Networks-Based Approaches -- 4.2 Decision Tree-Based Approaches -- 4.3 Bayesian Networks-Based Approaches -- 4.4 Support-Vector Machine-Based Approaches -- 4.5 Dependency Graph-Based Approaches -- 4.6 Other Approaches -- 4.7 Summary and Perspective -- 5 Conclusion -- References -- Deep Bidirectional Gated Recurrent Unit for Botnet Detection in Smart Homes -- 1 Introduction -- 2 Deep BGRU Method for Botnet Detection in IoT Networks -- 2.1 Bidirectional Gated Recurrent Unit -- 2.2 The Proposed Method for Selection of Optimal BGRU Hyperparameters -- 2.3 Deep BGRU Classifier for IoT Botnet Detection -- 3 Results and Discussion -- 3.1 Influence of Activation Functions on Classification Performance -- 3.2 Influence of the Number of Epochs on Classification Performance -- 3.3 Influence of the Number of Hidden Layers on Classification Performance -- 3.4 Influence of Hidden Units on Classification Performance -- 3.5 Influence of Batch Size on Classification Performance -- 3.6 Influence of Optimizers on Classification |
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