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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 managementHow different advanced AI and machine learning techniques can be useful and helpful to facilitate network automationHow the introduced techniques can be applied to many other related network and security management tasksNetwork 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.

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.

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.

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 networkin 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.