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Online Public Access Catalogue

题名/责任者:

Cyber security and business intelligence : innovations and machine learning for cyber risk management / edited by Mohammad Zoynul Abedin and Petr Hajek.

出版发行项:

Abingdon, Oxon ; New York, NY : Routledge, 2024.

ISBN:

9781032259574

ISBN:

9781032259581

载体形态项:

xii, 222 pages : illustrations ; 24 cm.

丛编说明:

Routledge studies in innovation, organizations and technology

附加个人名称:

Abedin, Mohammad Zoynul, editor.

附加个人名称:

Hajek, Petr, editor.

论题主题:

Business intelligence.

论题主题:

Computer security.

论题主题:

Risk management.

论题主题:

Computer networks-Security measures.

论题主题:

Computer crimes-Prevention.

中图法分类号:

F270.7

书目附注:

Includes bibliographical references and index.

摘要附注:

"To cope with the competitive worldwide marketplace, organizations rely on business intelligence to an increasing extent. Cyber security is an inevitable practice to protect the entire business sector and its customer. This book presents the significance and application of cyber security for safeguarding organizations, individuals' personal information and government. The book provides both practical and managerial implications of cyber security that also supports business intelligence and discusses the latest innovations in cyber security. It offers a roadmap to master's degree students and Ph.D. researchers for cyber security analysis in order to minimize the cyber security risk and protect customers from cyber-attack. The book also introduces the most advanced and novel machine learning techniques including, but not limited to, Support Vector Machine, Neural Networks, Extreme Learning Machine, Ensemble Learning, and Deep Learning Approaches, with a goal to apply those on cyber risk management datasets. It will also leverage real-world financial instances to practice business product modelling and data analysis. The contents of this book will be useful for a wide audience who are involved in managing network systems, data security, data forecasting, cyber risk modelling, fraudulent credit risk detection, portfolio management, and data regulatory bodies. It will be particularly beneficial to academics as well as practitioners who are looking to protect their IT system, reduce data breaches and cyber-attack vulnerabilities"--

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