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.
