Cloud Computing Anomaly and Threat Detection: Using Big Data Analytics and Machine Learning - Couverture souple

Muzaferija, Ibrahim

9786204719047: Cloud Computing Anomaly and Threat Detection: Using Big Data Analytics and Machine Learning

Couverture souple

ISBN 10 : 6204719041 ISBN 13 : 9786204719047

Editeur : LAP LAMBERT Academic Publishing, 2021

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While leveraging cloud computing for large-scale distributed applications allows seamless scaling, many companies struggle following up with the amount of data generated in terms of efficient processing and anomaly detection. With the rapid growth of web attacks, anomaly detection becomes a necessary part of the management of modern large-scale distributed web applications. As the record of user behavior, weblogs certainly become the research object related to anomaly detection. Many anomaly detection methods based on automated log analysis have been proposed. However, not in the context of big data applications where normal and anomalous behavior models need to be constructed before prediction attempts.To address this problem, Big Data Analytics and Machine Learning algorithms in overcoming the challenges of data processing, pattern detection, and anomaly prediction in large and high-dimensional data representing user and application logs are utilized.

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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2021
Langue: anglais
ISBN 10: 6204719041
ISBN 13: 9786204719047
Reliure: Broch�
Nombre de pages: 112
Coordonn�es du fabricant: non disponible
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Cloud Computing Anomaly and Threat Detection

Ibrahim Muzaferija

Edit� par LAP LAMBERT Academic Publishing Nov 2021, 2021

ISBN 10 : 6204719041 ISBN 13 : 9786204719047

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -While leveraging cloud computing for large-scale distributed applications allows seamless scaling, many companies struggle following up with the amount of data generated in terms of efficient processing and anomaly detection. With the rapid growth of web attacks, anomaly detection becomes a necessary part of the management of modern large-scale distributed web applications. As the record of user behavior, weblogs certainly become the research object related to anomaly detection. Many anomaly detection methods based on automated log analysis have been proposed. However, not in the context of big data applications where normal and anomalous behavior models need to be constructed before prediction attempts.To address this problem, Big Data Analytics and Machine Learning algorithms in overcoming the challenges of data processing, pattern detection, and anomaly prediction in large and high-dimensional data representing user and application logs are utilized. 112 pp. Englisch. N� de r�f. du vendeur 9786204719047

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Cloud Computing Anomaly and Threat Detection

Ibrahim Muzaferija

Edit� par LAP LAMBERT Academic Publishing, 2021

ISBN 10 : 6204719041 ISBN 13 : 9786204719047

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Muzaferija IbrahimIbrahim Muzaferija is a researcher, experienced software engineer and machine learning specialist, currently working as a data scientist. He creates links between academia and industry, solves cross-industry problem. N� de r�f. du vendeur 537543957

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Cloud Computing Anomaly and Threat Detection

Ibrahim Muzaferija

Edit� par LAP LAMBERT Academic Publishing Nov 2021, 2021

ISBN 10 : 6204719041 ISBN 13 : 9786204719047

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -While leveraging cloud computing for large-scale distributed applications allows seamless scaling, many companies struggle following up with the amount of data generated in terms of efficient processing and anomaly detection. With the rapid growth of web attacks, anomaly detection becomes a necessary part of the management of modern large-scale distributed web applications. As the record of user behavior, weblogs certainly become the research object related to anomaly detection. Many anomaly detection methods based on automated log analysis have been proposed. However, not in the context of big data applications where normal and anomalous behavior models need to be constructed before prediction attempts.To address this problem, Big Data Analytics and Machine Learning algorithms in overcoming the challenges of data processing, pattern detection, and anomaly prediction in large and high-dimensional data representing user and application logs are utilized.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 112 pp. Englisch. N� de r�f. du vendeur 9786204719047

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Cloud Computing Anomaly and Threat Detection : Using Big Data Analytics and Machine Learning

Ibrahim Muzaferija

Edit� par LAP LAMBERT Academic Publishing, 2021

ISBN 10 : 6204719041 ISBN 13 : 9786204719047

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - While leveraging cloud computing for large-scale distributed applications allows seamless scaling, many companies struggle following up with the amount of data generated in terms of efficient processing and anomaly detection. With the rapid growth of web attacks, anomaly detection becomes a necessary part of the management of modern large-scale distributed web applications. As the record of user behavior, weblogs certainly become the research object related to anomaly detection. Many anomaly detection methods based on automated log analysis have been proposed. However, not in the context of big data applications where normal and anomalous behavior models need to be constructed before prediction attempts.To address this problem, Big Data Analytics and Machine Learning algorithms in overcoming the challenges of data processing, pattern detection, and anomaly prediction in large and high-dimensional data representing user and application logs are utilized. N� de r�f. du vendeur 9786204719047

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