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Examines the design and use of Intrusion Detection Systems (IDS) to secure Supervisory Control and Data Acquisition (SCADA) systems Cyber-attacks on SCADA systems—the control system architecture that uses computers, networked data communications, and graphical user interfaces for high-level process supervisory management—can lead to costly financial consequences or even result in loss of life. Minimizing potential risks and responding to malicious actions requires innovative approaches for monitoring SCADA systems and protecting them from targeted attacks. SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is designed to help security and networking professionals develop and deploy accurate and effective Intrusion Detection Systems (IDS) for SCADA systems that leverage autonomous machine learning. Providing expert insights, practical advice, and up-to-date coverage of developments in SCADA security, this authoritative guide presents a new approach for efficient unsupervised IDS driven by SCADA-specific data. Organized into eight in-depth chapters, the text first discusses how traditional IT attacks can also be possible against SCADA, and describes essential SCADA concepts, systems, architectures, and main components. Following chapters introduce various SCADA security frameworks and approaches, including evaluating security with virtualization-based SCADAVT, using SDAD to extract proximity-based detection, finding a global and efficient anomaly threshold with GATUD, and more. This important book: Provides diverse perspectives on establishing an efficient IDS approach that can be implemented in SCADA systemsDescribes the relationship between main components and three generations of SCADA systemsExplains the classification of a SCADA IDS based on its architecture and implementationSurveys the current literature in the field and suggests possible directions for future researchSCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is a must-read for all SCADA security and networking researchers, engineers, system architects, developers, managers, lecturers, and other SCADA security industry practitioners.

Examines the design and use of Intrusion Detection Systems (IDS) to secure Supervisory Control and Data Acquisition (SCADA) systems

Cyber-attacks on SCADA systems—the control system architecture that uses computers, networked data communications, and graphical user interfaces for high-level process supervisory management—can lead to costly financial consequences or even result in loss of life. Minimizing potential risks and responding to malicious actions requires innovative approaches for monitoring SCADA systems and protecting them from targeted attacks. SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is designed to help security and networking professionals develop and deploy accurate and effective Intrusion Detection Systems (IDS) for SCADA systems that leverage autonomous machine learning.

Providing expert insights, practical advice, and up-to-date coverage of developments in SCADA security, this authoritative guide presents a new approach for efficient unsupervised IDS driven by SCADA-specific data. Organized into eight in-depth chapters, the text first discusses how traditional IT attacks can also be possible against SCADA, and describes essential SCADA concepts, systems, architectures, and main components. Following chapters introduce various SCADA security frameworks and approaches, including evaluating security with virtualization-based SCADAVT, using SDAD to extract proximity-based detection, finding a global and efficient anomaly threshold with GATUD, and more. This important book:

Provides diverse perspectives on establishing an efficient IDS approach that can be implemented in SCADA systems Describes the relationship between main components and three generations of SCADA systems Explains the classification of a SCADA IDS based on its architecture and implementation Surveys the current literature in the field and suggests possible directions for future research

SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is a must-read for all SCADA security and networking researchers, engineers, system architects, developers, managers, lecturers, and other SCADA security industry practitioners.

Examines the design and use of Intrusion Detection Systems (IDS) to secure Supervisory Control and Data Acquisition (SCADA) systems

Cyber-attacks on SCADA systems—the control system architecture that uses computers, networked data communications, and graphical user interfaces for high-level process supervisory management—can lead to costly financial consequences or even result in loss of life. Minimizing potential risks and responding to malicious actions requires innovative approaches for monitoring SCADA systems and protecting them from targeted attacks. SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is designed to help security and networking professionals develop and deploy accurate and effective Intrusion Detection Systems (IDS) for SCADA systems that leverage autonomous machine learning.

Providing expert insights, practical advice, and up-to-date coverage of developments in SCADA security, this authoritative guide presents a new approach for efficient unsupervised IDS driven by SCADA-specific data. Organized into eight in-depth chapters, the text first discusses how traditional IT attacks can also be possible against SCADA, and describes essential SCADA concepts, systems, architectures, and main components. Following chapters introduce various SCADA security frameworks and approaches, including evaluating security with virtualization-based SCADAVT, using SDAD to extract proximity-based detection, finding a global and efficient anomaly threshold with GATUD, and more. This important book:

Provides diverse perspectives on establishing an efficient IDS approach that can be implemented in SCADA systems Describes the relationship between main components and three generations of SCADA systems Explains the classification of a SCADA IDS based on its architecture and implementation Surveys the current literature in the field and suggests possible directions for future research

SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention is a must-read for all SCADA security and networking researchers, engineers, system architects, developers, managers, lecturers, and other SCADA security industry practitioners.

With the massive increase of data and traffic on the Internet within the 5G, IoT and smart cities frameworks, current network classification and analysis techniques are falling short. Novel approaches using machine learning algorithms are needed to cope with and manage real-world network traffic, including supervised, semi-supervised, and unsupervised classification techniques. Accurate and effective classification of network traffic will lead to better quality of service and more secure and manageable networks.This authored book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. The authors explore novel methods for enhancing network statistics at the transport layer, helping to identify optimal feature selection through a global optimization approach and providing automatic labelling for raw traffic through a SemTra framework to maintain provable privacy on information disclosure properties.

This book constitutes the refereed post-conference proceedings of the 17th EAI International Conference on Quality, Reliability, Security and Robustness in Heterogeneous Networks, QShine 2021, held in November 2020. Due to COVID-19 pandemic the conference was held virtually.The 20 revised full papers were carefully reviewed and selected from 43 submissions. The papers are organized thematically in tracks Machine Learning in Distributed Networks; 5G Networks and Security; IoT Security and Lightweight Cryptography; Network Security; and Privacy-preserving Emerging Networked Applications.

This book constitutes the refereed post-conference proceedings of the 17th EAI International Conference on Quality, Reliability, Security and Robustness in Heterogeneous Networks, QShine 2021, held in November 2020. Due to COVID-19 pandemic the conference was held virtually. The 20 revised full papers were carefully reviewed and selected from 43 submissions. The papers are organized thematically in tracks Machine Learning in Distributed Networks; 5G Networks and Security; IoT Security and Lightweight Cryptography; Network Security; and Privacy-preserving Emerging Networked Applications.

This book deals with Private Information Retrieval (PIR), a technique allowing a user to retrieve an element from a server in possession of a database without revealing to the server which element is retrieved. PIR has been widely applied to protect the privacy of the user in querying a service provider on the Internet. For example, by PIR, one can query a location-based service provider about the nearest car park without revealing his location to the server. The first PIR approach was introduced by Chor, Goldreich, Kushilevitz and Sudan in 1995 in a multi-server setting, where the user retrieves information from multiple database servers, each of which has a copy of the same database. To ensure user privacy in the multi-server setting, the servers must be trusted not to collude. In 1997, Kushilevitz and Ostrovsky constructed the first single-database PIR. Since then, many efficient PIR solutions have been discovered. Beginning with a thorough survey of single-database PIR techniques,this text focuses on the latest technologies and applications in the field of PIR. The main categories are illustrated with recently proposed PIR-based solutions by the authors. Because of the latest treatment of the topic, this text will be highly beneficial to researchers and industry professionals in information security and privacy.

This book deals with Private Information Retrieval (PIR), a technique allowing a user to retrieve an element from a server in possession of a database without revealing to the server which element is retrieved. PIR has been widely applied to protect the privacy of the user in querying a service provider on the Internet. For example, by PIR, one can query a location-based service provider about the nearest car park without revealing his location to the server. The first PIR approach was introduced by Chor, Goldreich, Kushilevitz and Sudan in 1995 in a multi-server setting, where the user retrieves information from multiple database servers, each of which has a copy of the same database. To ensure user privacy in the multi-server setting, the servers must be trusted not to collude. In 1997, Kushilevitz and Ostrovsky constructed the first single-database PIR. Since then, many efficient PIR solutions have been discovered. Beginning with a thorough survey of single-database PIR techniques,this text focuses on the latest technologies and applications in the field of PIR. The main categories are illustrated with recently proposed PIR-based solutions by the authors. Because of the latest treatment of the topic, this text will be highly beneficial to researchers and industry professionals in information security and privacy.

This monograph provides a comprehensive and rigorous exposition of the basic concepts and most important modern research results concerning blockchain and its applications. The book includes the required cryptographic fundamentals underpinning the blockchain technology, since understanding of the concepts of cryptography involved in the design of blockchain is necessary for mastering the security guarantees furnished by blockchain. It also contains an introduction to cryptographic primitives, and separate chapters on bitcoin, ethereum and smart contracts, public blockchain, private blockchain, cryptocurrencies, and blockchain applications.This volume is of great interest to active researchers who are keen to develop novel applications of blockchain in the field of their investigatio. Further, it is also beneficial for  industry practitioners as well as undergraduate students in computing and information technology.

This monograph provides a comprehensive and rigorous exposition of the basic concepts and most important modern research results concerning blockchain and its applications. The book includes the required cryptographic fundamentals underpinning the blockchain technology, since understanding of the concepts of cryptography involved in the design of blockchain is necessary for mastering the security guarantees furnished by blockchain. It also contains an introduction to cryptographic primitives, and separate chapters on bitcoin, ethereum and smart contracts, public blockchain, private blockchain, cryptocurrencies, and blockchain applications.This volume is of great interest to active researchers who are keen to develop novel applications of blockchain in the field of their investigatio. Further, it is also beneficial for  industry practitioners as well as undergraduate students in computing and information technology.

This book provides a comprehensive exploration of advanced privacy-preserving methods, ensuring secure data processing across various domains. This book also delves into key technologies such as homomorphic encryption, secure multiparty computation, and differential privacy, discussing their theoretical foundations, implementation challenges, and real-world applications in cloud computing, blockchain, artificial intelligence, and healthcare. With the rapid growth of digital technologies, data privacy has become a critical concern for individuals, businesses, and governments.The chapters cover fundamental cryptographic principles and extend into applications in privacy-preserving data mining, secure machine learning, and privacy-aware social networks. By combining state-of-the-art techniques with practical case studies, this book serves as a valuable resource for those navigating the evolving landscape of data privacy and security.Designed to bridge theory and practice, this book is tailored for researchers and graduate students focused on this field. Industry professionals seeking an in-depth understanding of privacy-enhancing technologies will also want to purchase this book.

This book provides a comprehensive exploration of advanced privacy-preserving methods, ensuring secure data processing across various domains. This book also delves into key technologies such as homomorphic encryption, secure multiparty computation, and differential privacy, discussing their theoretical foundations, implementation challenges, and real-world applications in cloud computing, blockchain, artificial intelligence, and healthcare. With the rapid growth of digital technologies, data privacy has become a critical concern for individuals, businesses, and governments. The chapters cover fundamental cryptographic principles and extend into applications in privacy-preserving data mining, secure machine learning, and privacy-aware social networks. By combining state-of-the-art techniques with practical case studies, this book serves as a valuable resource for those navigating the evolving landscape of data privacy and security. Designed to bridge theory and practice, this book is tailored for researchers and graduate students focused on this field. Industry professionals seeking an in-depth understanding of privacy-enhancing technologies will also want to purchase this book.

This book introduces the fundamental concepts of homomorphic encryption. From these foundations, applications are developed in the fields of private information retrieval, private searching on streaming data, privacy-preserving data mining, electronic voting and cloud computing. The content is presented in an instructional and practical style, with concrete examples to enhance the reader's understanding. This volume achieves a balance between the theoretical and the practical components of modern information security. Readers will learn key principles of homomorphic encryption as well as their application in solving real world problems.