Quan Z. Sheng – författare

While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques.

This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals.

Key Features:

While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques.

This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals.

Key Features:

The text highlights a comprehensive survey that focuses on all security aspects and challenges facing the Internet of Things systems, including outsourcing techniques for partial computations on edge or cloud while presenting case studies to map security challenges. It further covers three security aspects including Internet of Things device identification and authentication, network traffic intrusion detection, and executable malware files detection.

This book:

It will serve as an ideal text for senior undergraduate and graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

The text highlights a comprehensive survey that focuses on all security aspects and challenges facing the Internet of Things systems, including outsourcing techniques for partial computations on edge or cloud while presenting case studies to map security challenges. It further covers three security aspects including Internet of Things device identification and authentication, network traffic intrusion detection, and executable malware files detection.

This book:

It will serve as an ideal text for senior undergraduate and graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

This book delves into the critical realm of trust management within the Internet of Vehicles (IOV) networks, exploring its multifaceted implications on safety and security which forms part of the intelligent transportation system domain.

IoV emerges as a powerful convergence, seamlessly amalgamating the Internet of Things (IoT) and the intelligent transportation systems (ITS). This is crucial not only for safety-critical applications but is also an indispensable resource for non-safety applications and efficient traffic flows. While this paradigm holds numerous advantages, the existence of malicious entities and the potential spread of harmful information within the network not only impairs its performance but also presents a danger to both passengers and pedestrians. Exploring the complexities arising from dynamicity and malicious actors, this book focuses primarily on modern trust management models designed to pinpoint and eradicate threats. This includes tackling the challenges regarding the quantification of trust attributes, corresponding weights of these attributes, and misbehavior detection threshold definition within the dynamic and distributed IoV environment.

This will serve as an essential guide for industry professionals and researchers working in the areas of automotive systems and transportation networks. Additionally, it will also be useful as a supplementary text for students enrolled in courses covering cybersecurity, communication networks, and human factors in transportation.

Sarah Ali Siddiqui is a CSIRO Early Research Career (CERC) Fellow in the Cyber Security Automation and Orchestration Team, Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.

Adnan Mahmood is a Lecturer in Computing – IoT and Networking at the School of Computing, Macquarie University, Sydney, Australia.

Quan Z. (Michael) Sheng is a Distinguished Professor and Head of the School of Computing, at Macquarie University, Sydney, Australia.

Hajime Suzuki is a Principal Research Scientist at the Cybersecurity & Quantum Systems Group, Software and Computational Systems Research Program, Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.

Wei Ni is a Principal Scientist at the Commonwealth Scientific and Industrial Research Organisation, a Technical Expert at Standards Australia, a Conjoint Pro-fessor at the University of New South Wales, an Adjunct Professor at the University of Technology Sydney, and an Honorary Professor at Macquarie University, Sydney, Australia.

This book delves into the critical realm of trust management within the Internet of Vehicles (IOV) networks, exploring its multifaceted implications on safety and security which forms part of the intelligent transportation system domain.

IoV emerges as a powerful convergence, seamlessly amalgamating the Internet of Things (IoT) and the intelligent transportation systems (ITS). This is crucial not only for safety-critical applications but is also an indispensable resource for non-safety applications and efficient traffic flows. While this paradigm holds numerous advantages, the existence of malicious entities and the potential spread of harmful information within the network not only impairs its performance but also presents a danger to both passengers and pedestrians. Exploring the complexities arising from dynamicity and malicious actors, this book focuses primarily on modern trust management models designed to pinpoint and eradicate threats. This includes tackling the challenges regarding the quantification of trust attributes, corresponding weights of these attributes, and misbehavior detection threshold definition within the dynamic and distributed IoV environment.

This will serve as an essential guide for industry professionals and researchers working in the areas of automotive systems and transportation networks. Additionally, it will also be useful as a supplementary text for students enrolled in courses covering cybersecurity, communication networks, and human factors in transportation.

Sarah Ali Siddiqui is a CSIRO Early Research Career (CERC) Fellow in the Cyber Security Automation and Orchestration Team, Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.

Adnan Mahmood is a Lecturer in Computing – IoT and Networking at the School of Computing, Macquarie University, Sydney, Australia.

Quan Z. (Michael) Sheng is a Distinguished Professor and Head of the School of Computing, at Macquarie University, Sydney, Australia.

Hajime Suzuki is a Principal Research Scientist at the Cybersecurity & Quantum Systems Group, Software and Computational Systems Research Program, Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.

Wei Ni is a Principal Scientist at the Commonwealth Scientific and Industrial Research Organisation, a Technical Expert at Standards Australia, a Conjoint Pro-fessor at the University of New South Wales, an Adjunct Professor at the University of Technology Sydney, and an Honorary Professor at Macquarie University, Sydney, Australia.

This book examines the uses and potential risks of location-based services (LBS) in the context of big data, with a focus on location privacy protection methods.

The growth of the mobile Internet and the popularity of smart devices have spurred the development of LBS and related mobile applications. However, the misuse of sensitive location data could compromise the physical and communication security of associated devices and nodes, potentially leading to privacy breaches. This book explores the potential risks to the location privacy of mobile users in the context of big data applications. It discusses the latest methods and implications of location privacy from different perspectives. The author offers case studies of three applications: statistical disclosure and privacy protection of location-based big data using a centralized differential privacy model; a user location perturbation mechanism based on a localized differential privacy model; and terminal location perturbation using a geo-indistinguishability model. Linking recent developments in three-dimensional positioning and artificial intelligence, the book also predicts future trends and provides insights into research issues in location privacy.

This title will be a valuable resource for researchers, students, and professionals interested in location-based services, privacy computing and protection, wireless network security, and big data security.

This book examines the uses and potential risks of location-based services (LBS) in the context of big data, with a focus on location privacy protection methods.

The growth of the mobile Internet and the popularity of smart devices have spurred the development of LBS and related mobile applications. However, the misuse of sensitive location data could compromise the physical and communication security of associated devices and nodes, potentially leading to privacy breaches. This book explores the potential risks to the location privacy of mobile users in the context of big data applications. It discusses the latest methods and implications of location privacy from different perspectives. The author offers case studies of three applications: statistical disclosure and privacy protection of location-based big data using a centralized differential privacy model; a user location perturbation mechanism based on a localized differential privacy model; and terminal location perturbation using a geo-indistinguishability model. Linking recent developments in three-dimensional positioning and artificial intelligence, the book also predicts future trends and provides insights into research issues in location privacy.

This title will be a valuable resource for researchers, students, and professionals interested in location-based services, privacy computing and protection, wireless network security, and big data security.

Web services and Service-Oriented Computing (SOC) have become thriving areas of academic research, joint university/industry research projects, and novel IT products on the market. SOC is the computing paradigm that uses Web services as building blocks for the engineering of composite, distributed applications out of the reusable application logic encapsulated by Web services. Web services could be considered the best-known and most standardized technology in use today for distributed computing over the Internet.

This book is the second installment of a two-book collection covering the state-of-the-art of both theoretical and practical aspects of Web services and SOC research and deployments. Advanced Web Services specifically focuses on advanced topics of Web services and SOC and covers topics including Web services transactions, security and trust, Web service management, real-world case studies, and novel perspectives and future directions.

The editors present foundational topics in the first book of the collection, Web Services Foundations (Springer, 2013). Together, both books comprise approximately 1400 pages and are the result of an enormous community effort that involved more than 100 authors, comprising the world’s leading experts in this field.