This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity.

This book constitutes the refereed proceedings of the 4th International Workshop on Practice and Theory in Public Key Cryptography, PKC 2001, held in Cheju Island, Korea in February 2001.The 30 revised full papers presented were carefully reviewed and selected from 67 submissions. The papers address all current issues in public key cryptography, ranging from mathematical foundations to implementation issues.

This book constitutes the strictly refereed proceedings of the International Conference on the Theory and Applications of Cryptology and Information Security, ASIACRYPT '96, held in Kyongju, Korea, in November 1996.
The 31 revised full papers presented together with three invited contributions were carefully selected from a total of 124 submissions. The papers are organized in topical sections on discrete log based systems, efficient algorithms, hash functions and block cyphers, cryptographic protocols, signature and identification, visual secret sharing, key distribution, Boolean functions, electronic cash, special signatures, stream ciphers, and hard problems.

This book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocols with deep learning. Google and Microsoft announced a major investment in PPDL in early 2019. This was followed by Google's infamous announcement of "Private Join and Compute," an open source PPDL tools based on secure multi-party computation (secure MPC) and homomorphic encryption (HE) in June of that year. One of the challenging issues concerning PPDL is selecting its practical applicability despite the gap between the theory and practice. In order to solve this problem, it has recently been proposed that in addition to classical privacy-preserving methods (HE, secure MPC, differential privacy, secure enclaves), new federated or split learning for PPDL should also be applied. This concept involves building a cloud framework that enables collaborative learning while keeping training data on client devices. This successfully preserves privacy and while allowing the framework to be implemented in the real world. This book provides fundamental insights into privacy-preserving and deep learning, offering a comprehensive overview of the state-of-the-art in PPDL methods. It discusses practical issues, and leveraging federated or split-learning-based PPDL. Covering the fundamental theory of PPDL, the pros and cons of current PPDL methods, and addressing the gap between theory and practice in the most recent approaches, it is a valuable reference resource for a general audience, undergraduate and graduate students, as well as practitioners interested learning about PPDL from the scratch, and researchers wanting to explore PPDL for their applications.

This book constitutes the thoroughly refereed post-conference proceedings of the 17th International Conference on Information and Communications Security, ICISC 2015, held in Beijing, China, in December 2015. The 24 revised full papers and 19 short papers presented were carefully selected from 148 submissions. The papers provide the latest results in research and development in the field of information security and applied cryptology.

This book constitutes the refereed proceedings of the 4th International Conference on Security and Cryptology, ICISC 2001, held in Seoul, Korea, in December 2001. The 32 revised full papers presented together with one invited paper were carefully reviewed and selected from a total of 107 submissions. All current issues of cryptography and cryptanalysis and their applications to securing data, systems, and communications are addressed.