9786204719047 - Cloud Computing Anomaly and Threat Detection: Using Big Data Analytics and Machine Learning de Muzaferija, Ibrahim (5 resultados)

Taschenbuch. Condición: Neu. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 112 pp. Englisch.

Taschenbuch. Condición: Neu. Cloud Computing Anomaly and Threat Detection | Using Big Data Analytics and Machine Learning | Ibrahim Muzaferija | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204719047 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condición: 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.

Condición: 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.

Taschenbuch. Condición: 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.