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- PublisherLAP LAMBERT Academic Publishing
- Publication date2023
- ISBN 10 6206783200
- ISBN 13 9786206783206
- BindingPaperback
- LanguageEnglish
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Big Data Cyber Security Using Machine Learning
Published by
LAP LAMBERT Academic Publishing Sep 2023, 2023
ISBN 10: 6206783200
ISBN 13: 9786206783206
New
Taschenbuch
Seller: 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 -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. Seller Inventory # 9786206783206
Quantity: 2 available
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Big Data Cyber Security Using Machine Learning : Cyber Security
Published by
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206783200
ISBN 13: 9786206783206
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9786206783206
Quantity: 1 available
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Big Data Cyber Security Using Machine Learning
Published by
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206783200
ISBN 13: 9786206783206
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: 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. Seller Inventory # 1132722034
Quantity: Over 20 available
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Big Data Cyber Security Using Machine Learning
Published by
LAP LAMBERT Academic Publishing Sep 2023, 2023
ISBN 10: 6206783200
ISBN 13: 9786206783206
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 64 pp. Englisch. Seller Inventory # 9786206783206
Quantity: 2 available