Software-Defined Networks (SDN) work by virtualization of the network and the Cognitive Software-Defined Network (CSDN) combines the efficiencies of SDN with cognitive learning algorithms and enhanced protocols to automatize SDN. Partial deployment of SDN along with traditional networking devices forms a Hybrid Software-Defined Network (HSDN). Software-Defined Network Frameworks: Security Issues and Use Cases consolidates the research relating to the security in SDN, CSDN, and Hybrid SDNs. The security enhancements derived from the use of various SDN frameworks and the security challenges thus introduced, are also discussed. Overall, this book explains the different architectures of SDNs and the security challenges needed for implementing them.

Features:

• Illustrates different frameworks of SDN and their security issues in a single volume
• Discusses design and assessment of efficient SDN northbound/southbound interfaces
• Describes cognitive computing, affective computing, machine learning, and other novel tools
• Illustrates coupling of SDN and traditional networking – Hybrid SDN
• Explores services, technologies, algorithms, and methods for data analysis in CSDN

The book is aimed at researchers and graduate students in software engineering, network security, computer networks, high performance computing, communications engineering, and intelligent systems.

Software-Defined Networks (SDN) work by virtualization of the network and the Cognitive Software-Defined Network (CSDN) combines the efficiencies of SDN with cognitive learning algorithms and enhanced protocols to automatize SDN. Partial deployment of SDN along with traditional networking devices forms a Hybrid Software-Defined Network (HSDN). Software-Defined Network Frameworks: Security Issues and Use Cases consolidates the research relating to the security in SDN, CSDN, and Hybrid SDNs. The security enhancements derived from the use of various SDN frameworks and the security challenges thus introduced, are also discussed. Overall, this book explains the different architectures of SDNs and the security challenges needed for implementing them.

Features:

• Illustrates different frameworks of SDN and their security issues in a single volume
• Discusses design and assessment of efficient SDN northbound/southbound interfaces
• Describes cognitive computing, affective computing, machine learning, and other novel tools
• Illustrates coupling of SDN and traditional networking – Hybrid SDN
• Explores services, technologies, algorithms, and methods for data analysis in CSDN

The book is aimed at researchers and graduate students in software engineering, network security, computer networks, high performance computing, communications engineering, and intelligent systems.