Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

This book provides state-of-the-art coverage of the principles, techniques, and management of issues in cyber security, including threat attacks, privacy, signature and encryption schemes. One of the most important topics addressed concerns lightweight solutions for public key encryption in resource-constrained environments; the book highlights the latest developments in this area. Authentication is another central issue in cyber security. In this book, we address this aspect and sub-aspects ranging from cryptographic approaches to practical design issues, such as CAPTCHA. Privacy is another main topic that is discussed in detail, from techniques for enhancing privacy to pseudonymous schemes. Addressing key issues in the emerging field of cyber security, this book effectively bridges the gap between computer security and threat attacks, and showcases promising applications involving cryptography and security.

Blockchain technologies, as an emerging distributed architecture and computing paradigm, have accelerated the development/application of the Cloud/GPU/Edge Computing, Artificial Intelligence, cyber physical systems, social networking, crowdsourcing and crowdsensing, 5G, trust management, and finance. The popularity and rapid development of Blockchain brings many technical and regulatory challenges for research and academic communities. This book will feature contributions from experts on topics related to performance, benchmarking, durability, robustness, as well data gathering and management, algorithms, analytics techniques for transactions processing, and implementation of applications.

This book provides a thorough overview of the evolution of privacy-preserving machine learning schemes over the last ten years, after discussing the importance of privacy-preserving techniques. In response to the diversity of Internet services, data services based on machine learning are now available for various applications, including risk assessment and image recognition. In light of open access to datasets and not fully trusted environments, machine learning-based applications face enormous security and privacy risks. In turn, it presents studies conducted to address privacy issues and a series of proposed solutions for ensuring privacy protection in machine learning tasks involving multiple parties. In closing, the book reviews state-of-the-art privacy-preserving techniques and examines the security threats they face.

This book constitutes the proceedings of the 25th International Conference on Information Security, ISC 2022, which took place in Bali, Indonesia, in December 2022. The 21 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 72 submissions. The contributions were organized in topical sections as follows: Cryptography; Post-Quantum Cryptography; Cryptanalysis; Blockchain; Email and Web Security; Malware; and AI Security.

Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

This book constitutes the proceedings of the 8th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2022, which took place in Xi'an, China, in October 2022. The 23 papers presented in this volume were carefully reviewed and selected from 103 submissions. The papers were evaluated on the basis of their significance, novelty, technical quality, as well as on their practical impact or their level of advancement of the field's foundations. They were organized in topical sections as follows: Cryptography and its applications; Network security and privacy protection; Data detection; Blockchain and its applications.

This book LNCS 13547 constitutes the proceedings of the 14th International Symposium on Cyberspace Safety and Security, CSS 2022, held in Xi'an, China, in October 2022.The 26 revised full papers presented were carefully reviewed and selected from 104 initial submissions. The papers focus on Cyberspace Safety and Security, such as authentication, access control, availability, integrity, privacy, confidentiality, dependability and sustainability issues of cyberspace.

This book constitutes the refereed proceedings of the 4thInternational Conference on Security and Privacy in New Computing Environments, SPNCE 2021, held in December 2021. Due to COVID-19 pandemic the conference was held virtually. The 33 full papers were selected from 61 submissions and focus on security and privacy in new computing environments. The theme of SPNCE 2021 was "Secure Wireless Communication Systems: Infrastructure, Algorithms, and Management".

Blockchain technologies, as an emerging distributed architecture and computing paradigm, have accelerated the development/application of the Cloud/GPU/Edge Computing, Artificial Intelligence, cyber physical systems, social networking, crowdsourcing and crowdsensing, 5G, trust management, and finance. The popularity and rapid development of Blockchain brings many technical and regulatory challenges for research and academic communities. This book will feature contributions from experts on topics related to performance, benchmarking, durability, robustness, as well data gathering and management, algorithms, analytics techniques for transactions processing, and implementation of applications.

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi'an, China in October 2020.The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi'an, China in October 2020.The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi'an, China in October 2020.The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.

This book constitutes the thoroughly refereed post-conference proceedings of the 13th International Conference on Information Security and Cryptology, Inscrypt 2017, held in Xi'an, China, in November 2017. The 27 revised full papers presented together with 5 keynote speeches were carefully reviewed and selected from 80 submissions. The papers are organized in the following topical sections: cryptographic protocols and algorithms; digital signatures; encryption; cryptanalysis and attack; and applications.

This book constitutes the refereed proceedings of the 5th Provable Security Conference held in in Xi'an, China, in October 2011. The 22 full papers presented together with 4 short papers and 2 invited talks were carefully reviewed and selected from 75 submissions. The papers are divided in topical sections on cryptographic primitives; encryption; cryptographic protocols; security models and framework; and key agreement.

This book constitutes the proceedings of the Second International Conference on Machine Learning for Cyber Security, ML4CS 2019, held in Xi'an, China in September 2019. The 23 revised full papers and 3 short papers presented were carefully reviewed and selected from 70 submissions. The papers detail all aspects of machine learning in network infrastructure security, in network security detections and in application software security.