Big Data Cyber Security Using Machine Learning

Descripción

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.

N° de ref. del artículo 9786206783206

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Detalles bibliográficos

Título

Big Data Cyber Security Using Machine Learning

Editor

LAP LAMBERT Academic Publishing Sep 2023

Fecha de publicación

2023

Idioma

Inglés

ISBN 10

6206783200

ISBN 13

9786206783206

Encuadernación

Taschenbuch

Estado

Neu