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.