Big Data Cyber Security Using Machine Learning: Cyber Security - Couverture souple

Arun Kumar, Kandru ; Chinta, Anuradha ; Little Flower, Kunchala

9786206783206: Big Data Cyber Security Using Machine Learning: Cyber Security

Couverture souple

ISBN 10 : 6206783200 ISBN 13 : 9786206783206

Editeur : LAP LAMBERT Academic Publishing, 2023

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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2023
Langue: anglais
ISBN 10: 6206783200
ISBN 13: 9786206783206
Reliure: Broch�
Nombre de pages: 64
Coordonn�es du fabricant: non disponible
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Big Data Cyber Security Using Machine Learning

Arun Kumar, Kandru; Chinta, Anuradha; Little Flower, Kunchala

Edit� par LAP LAMBERT Academic Publishing, 2023

ISBN 10 : 6206783200 ISBN 13 : 9786206783206

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Big Data Cyber Security Using Machine Learning

Arun Kumar, Kandru; Chinta, Anuradha; Little Flower, Kunchala

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ISBN 10 : 6206783200 ISBN 13 : 9786206783206

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Big Data Cyber Security Using Machine Learning

Arun Kumar, Kandru; Chinta, Anuradha; Little Flower, Kunchala

Edit� par LAP LAMBERT Academic Publishing, 2023

ISBN 10 : 6206783200 ISBN 13 : 9786206783206

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Big Data Cyber Security Using Machine Learning

Kandru Arun Kumar

Edit� par LAP LAMBERT Academic Publishing Sep 2023, 2023

ISBN 10 : 6206783200 ISBN 13 : 9786206783206

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Cyber security in the context of big data is known to be a critical problem and presents a great challenge to the research community. Machine learning algorithms have been suggested as candidates for handling big data security problems. Among these algorithms, support vector machines (SVMs) have achieved remarkable success on various classification problems. However, to establish an effective SVM, the user needs to deny the proper SVM configuration in advance, which is a challenging task that requires expert knowledge and a large amount of manual effort for trial and error. Here we formulate the SVM configuration process as a bi-objective optimization problem in which accuracy and model complexity are considered as two conflicting objectives. We propose a novel hyper-heuristic framework for bi-objective optimization that is independent of the problem domain. This is the first time that a hyper-heuristic has been developed for this problem. The proposed hyper-heuristic framework consists of a high-level strategy and low-level heuristics. 64 pp. Englisch. N� de r�f. du vendeur 9786206783206

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Big Data Cyber Security Using Machine Learning

Kandru Arun Kumar|Anuradha Chinta|Kunchala Little Flower

Edit� par LAP LAMBERT Academic Publishing, 2023

ISBN 10 : 6206783200 ISBN 13 : 9786206783206

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Arun Kumar KandruDr Arun Kumar Kandru Assoc. Professor CSE at Malla Reddy Engineering College, having 13 years of teaching experience.Dr Anuradha Chinta Asst. Professor CSE at V R Siddhartha Engineering College, having 11 years of te. N� de r�f. du vendeur 1132722034

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Big Data Cyber Security Using Machine Learning

Kandru Arun Kumar

Edit� par LAP LAMBERT Academic Publishing Sep 2023, 2023

ISBN 10 : 6206783200 ISBN 13 : 9786206783206

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Cyber security in the context of big data is known to be a critical problem and presents a great challenge to the research community. Machine learning algorithms have been suggested as candidates for handling big data security problems. Among these algorithms, support vector machines (SVMs) have achieved remarkable success on various classification problems. However, to establish an effective SVM, the user needs to deny the proper SVM configuration in advance, which is a challenging task that requires expert knowledge and a large amount of manual effort for trial and error. Here we formulate the SVM configuration process as a bi-objective optimization problem in which accuracy and model complexity are considered as two conflicting objectives. We propose a novel hyper-heuristic framework for bi-objective optimization that is independent of the problem domain. This is the first time that a hyper-heuristic has been developed for this problem. The proposed hyper-heuristic framework consists of a high-level strategy and low-level heuristics.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 64 pp. Englisch. N� de r�f. du vendeur 9786206783206

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Big Data Cyber Security Using Machine Learning : Cyber Security

Kandru Arun Kumar

Edit� par LAP LAMBERT Academic Publishing, 2023

ISBN 10 : 6206783200 ISBN 13 : 9786206783206

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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Cyber security in the context of big data is known to be a critical problem and presents a great challenge to the research community. Machine learning algorithms have been suggested as candidates for handling big data security problems. Among these algorithms, support vector machines (SVMs) have achieved remarkable success on various classification problems. However, to establish an effective SVM, the user needs to deny the proper SVM configuration in advance, which is a challenging task that requires expert knowledge and a large amount of manual effort for trial and error. Here we formulate the SVM configuration process as a bi-objective optimization problem in which accuracy and model complexity are considered as two conflicting objectives. We propose a novel hyper-heuristic framework for bi-objective optimization that is independent of the problem domain. This is the first time that a hyper-heuristic has been developed for this problem. The proposed hyper-heuristic framework consists of a high-level strategy and low-level heuristics. N� de r�f. du vendeur 9786206783206

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