Yulei Wu's Books

AI and Machine Learning for Network and Security Management

By: Yulei Wu,Jingguo Ge,Tong Li

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AI and Machine Learning for Network and Security Management

By: Yulei Wu,Jingguo Ge,Tong Li

AI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENT Extensive Resource for Understanding Key Tasks of Network and Security Management AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit. Sample ideas covered in this thought-provoking work include: How cognitive means, e.g., knowledge transfer, can help with network and security management How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation How the introduced techniques can be applied to many other related network and security management tasks Network engineers, content service providers, and cybersecurity service providers can use AI and Machine Learning for Network and Security Management to make better and more informed decisions in their areas of specialization. Students in a variety of related study programs will also derive value from the work by gaining a base understanding of historical foundational knowledge and seeing the key recent developments that have been made in the field.

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Page Count

308

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119835895

ISBN 13

9781119835899

Accountability and Privacy in Network Security

By: Yuxiang Ma,Yulei Wu,Jingguo Ge

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Accountability and Privacy in Network Security

By: Yuxiang Ma,Yulei Wu,Jingguo Ge

This book discusses accountability and privacy in network security from a technical perspective, providing a comprehensive overview of the latest research, as well as the current challenges and open issues. Further, it proposes a set of new and innovative solutions to balance privacy and accountability in networks in terms of their content, flow and service, using practical deep learning techniques for encrypted traffic analysis and focusing on the application of new technologies and concepts. These solutions take into account various key components (e.g. the in-network cache) in network architectures and adopt the emerging blockchain technique to ensure the security and scalability of the proposed architectures. In addition, the book examines in detail related studies on accountability and privacy, and validates the architectures using real-world datasets. Presenting secure and scalable solutions that can detect malicious behaviors in the network in a timely manner without compromising user privacy, the book offers a valuable resource for undergraduate and graduate students, researchers, and engineers working in the fields of network architecture and cybersecurity.

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Page Count

146

Publisher

Springer Nature

Published Date

ISBN 10

9811565759

ISBN 13

9789811565755

Multiple Imputation of Missing Data in Practice

Basic Theory and Analysis Strategies

By: Yulei He,Guangyu Zhang,Chiu-Hsieh Hsu

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Multiple Imputation of Missing Data in Practice

Basic Theory and Analysis Strategies

By: Yulei He,Guangyu Zhang,Chiu-Hsieh Hsu

Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis. Over the past 40 years or so, multiple imputation has gone through rapid development in both theories and applications. It is nowadays the most versatile, popular, and effective missing-data strategy that is used by researchers and practitioners across different fields. There is a strong need to better understand and learn about multiple imputation in the research and practical community. Accessible to a broad audience, this book explains statistical concepts of missing data problems and the associated terminology. It focuses on how to address missing data problems using multiple imputation. It describes the basic theory behind multiple imputation and many commonly-used models and methods. These ideas are illustrated by examples from a wide variety of missing data problems. Real data from studies with different designs and features (e.g., cross-sectional data, longitudinal data, complex surveys, survival data, studies subject to measurement error, etc.) are used to demonstrate the methods. In order for readers not only to know how to use the methods, but understand why multiple imputation works and how to choose appropriate methods, simulation studies are used to assess the performance of the multiple imputation methods. Example datasets and sample programming code are either included in the book or available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book). Key Features Provides an overview of statistical concepts that are useful for better understanding missing data problems and multiple imputation analysis Provides a detailed discussion on multiple imputation models and methods targeted to different types of missing data problems (e.g., univariate and multivariate missing data problems, missing data in survival analysis, longitudinal data, complex surveys, etc.) Explores measurement error problems with multiple imputation Discusses analysis strategies for multiple imputation diagnostics Discusses data production issues when the goal of multiple imputation is to release datasets for public use, as done by organizations that process and manage large-scale surveys with nonresponse problems For some examples, illustrative datasets and sample programming code from popular statistical packages (e.g., SAS, R, WinBUGS) are included in the book. For others, they are available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book)

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Page Count

495

Publisher

CRC Press

Published Date

ISBN 10

1498722075

ISBN 13

9781498722070

AI and Machine Learning for Network and Security Management

By: Yulei Wu,Jingguo Ge,Tong Li

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AI and Machine Learning for Network and Security Management

By: Yulei Wu,Jingguo Ge,Tong Li

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Solve jigsaw puzzle

Age

Page Count

308

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119835895

ISBN 13

9781119835899

Accountability and Privacy in Network Security

By: Yuxiang Ma,Yulei Wu,Jingguo Ge

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Accountability and Privacy in Network Security

By: Yuxiang Ma,Yulei Wu,Jingguo Ge

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Page Count

146

Publisher

Springer Nature

Published Date

ISBN 10

9811565759

ISBN 13

9789811565755

Big Data and Computational Intelligence in Networking

By: Yulei Wu,Fei Hu,Geyong Min,Albert Y. Zomaya

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Big Data and Computational Intelligence in Networking

By: Yulei Wu,Fei Hu,Geyong Min,Albert Y. Zomaya

This book presents state-of-the-art solutions to the theoretical and practical challenges stemming from the leverage of big data and its computational intelligence in supporting smart network operation, management, and optimization. In particular, the technical focus covers the comprehensive understanding of network big data, efficient collection and management of network big data, distributed and scalable online analytics for network big data, and emerging applications of network big data for computational intelligence.

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Page Count

529

Published Date

ISBN 10

1351642200

ISBN 13

9781351642200

Book's by Yulei Wu

AI and Machine Learning for Network and Security Management

AI and Machine Learning for Network and Security Management

Accountability and Privacy in Network Security

Accountability and Privacy in Network Security

Multiple Imputation of Missing Data in Practice

Multiple Imputation of Missing Data in Practice

AI and Machine Learning for Network and Security Management

AI and Machine Learning for Network and Security Management

Accountability and Privacy in Network Security

Accountability and Privacy in Network Security

Big Data and Computational Intelligence in Networking

Big Data and Computational Intelligence in Networking

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