Springer Accountability and Privacy in Network Security

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. From the Back Cover 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. About the Author Yuxiang Ma is an Associate Professor with the School of Computer and Information Engineering, Henan University. He received his B.S. degree in Network Engineering from Henan University in 2013 and his Ph.D. degree in Computer Architecture from the Computer Network Information Center, Chinese Academy of Sciences, in 2019. His research interests include computer network architectures and protocols, network security, and privacy enhancement technologies. He has published over 10 research papers. Yulei Wu is a Senior Lecturer with the Department of Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, UK. He received his B.Sc. degree (1st Class Hons.) in Computer Science and his Ph.D. degree in Computing and Mathematics from the University of Bradford, UK, in 2006 and 2010, respectively. His expertise is on networking, and his main research interests include computer networks, networked systems, software-defined networks and systems, network management, and network security and privacy. Dr. Wu contributes to major conferences on networking and networked systems as various roles, including Steering Committee Chair, General Chair, and Program Chair. His research has been supported by Engineering and Physical Sciences Research Council of UK, National Natural Science Foundation of China, and University's Innovation Platform and industry. He is an Editor of IEEE Transactions on Network and Service Management, Computer Networks (Elsevier), and IEEE Access. He is a Se