This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book * introduces novel machine-learning-based temporal normalization techniques * bridges research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition * provides detailed discussions of key research challenges and open research issues in gait biometrics recognition* compares biometrics systems trained and tested with the same footwear against those trained and tested with different footwear

This book introduces novel research targeting technical aspects of protecting information security and establishing trust in the digital space. New paradigms, and emerging threats and solutions are presented in topics such as application security and threat management; modern authentication paradigms; digital fraud detection; social engineering and insider threats; cyber threat intelligence; intrusion detection; behavioral biometrics recognition; hardware security analysis. The book presents both the important core and the specialized issues in the areas of protection, assurance, and trust in information security practice. It is intended to be a valuable resource and reference for researchers, instructors, students, scientists, engineers, managers, and industry practitioners.

This book presents the latest developments in biometrics technologies and reports on new approaches, methods, findings, and technologies developed or being developed by the research community and the industry. The book focuses on introducing fundamental principles and concepts of key enabling technologies for biometric systems applied for both physical and cyber security. The authors disseminate recent research and developing efforts in this area, investigate related trends and challenges, and present case studies and examples such as fingerprint, face, iris, retina, keystroke dynamics, and voice applications . The authors also investigate the advances and future outcomes in research and development in biometric security systems. The book is applicable to students, instructors, researchers, industry practitioners, and related government agencies staff. Each chapter is accompanied by a set of PowerPoint slides for use by instructors.

User authentication is the process of verifying whether the identity of a user is genuine prior to granting him or her access to resources or services in a secured environment. Traditionally, user authentication is performed statically at the point of entry of the system; however, continuous authentication (CA) seeks to address the shortcomings of this method by providing increased session security and combating insider threat. Continuous Authentication Using Biometrics: Data, Models, and Metrics presents chapters on continuous authentication using biometrics that have been contributed by the leading experts in this recent, fast growing research area. These chapters collectively provide a thorough and concise introduction to the field of biometric-based continuous authentication. The book covers the conceptual framework underlying continuous authentication and presents detailed processing models for various types of practical continuous authentication applications.

This book presents advances in security assurance for cyber-physical systems (CPS) and report on new machine learning (ML) and artificial intelligence (AI) approaches and technologies developed by the research community and the industry to address the challenges faced by this emerging field. Cyber-physical systems bridge the divide between cyber and physical-mechanical systems by combining seamlessly software systems, sensors, and actuators connected over computer networks. Through these sensors, data about the physical world can be captured and used for smart autonomous decision-making. This book introduces fundamental AI/ML principles and concepts applied in developing secure and trustworthy CPS, disseminates recent research and development efforts in this fascinating area, and presents relevant case studies, examples, and datasets. We believe that it is a valuable reference for students, instructors, researchers, industry practitioners, and related government agencies staff.

This book constitutes the refereed proceedings of the Second International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, ISDDC 2018, held in Vancouver, BC, Canada, in November 2018. The 10 full papers were carefully reviewed and selected from 28 submissions. This book also contains the abstracts of two keynote talks and one tutorial. The contributions included in this proceedings cover many aspects of theory and application of effective and efficient paradigms, approaches, and tools for building, maintaining, and managing secure and dependable systems and infrastructures, such as botnet detection, secure cloud computing and cryptosystems, IoT security, sensor and social network security, behavioral systems and data science, and mobile computing.

This book presents the latest developments in biometrics technologies and reports on new approaches, methods, findings, and technologies developed or being developed by the research community and the industry. The book focuses on introducing fundamental principles and concepts of key enabling technologies for biometric systems applied for both physical and cyber security. The authors disseminate recent research and developing efforts in this area, investigate related trends and challenges, and present case studies and examples such as fingerprint, face, iris, retina, keystroke dynamics, and voice applications . The authors also investigate the advances and future outcomes in research and development in biometric security systems. The book is applicable to students, instructors, researchers, industry practitioners, and related government agencies staff. Each chapter is accompanied by a set of PowerPoint slides for use by instructors.

This book introduces novel research targeting technical aspects of protecting information security and establishing trust in the digital space. New paradigms, and emerging threats and solutions are presented in topics such as application security and threat management; modern authentication paradigms; digital fraud detection; social engineering and insider threats; cyber threat intelligence; intrusion detection; behavioral biometrics recognition; hardware security analysis. The book presents both the important core and the specialized issues in the areas of protection, assurance, and trust in information security practice. It is intended to be a valuable resource and reference for researchers, instructors, students, scientists, engineers, managers, and industry practitioners.

This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book * introduces novel machine-learning-based temporal normalization techniques * bridges research gaps concerning the effect of footwear and stepping speed on footstep GRF-based person recognition * provides detailed discussions of key research challenges and open research issues in gait biometrics recognition* compares biometrics systems trained and tested with the same footwear against those trained and tested with different footwear

This book constitutes the refereed proceedings of the First International Conference on Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments, ISDDC 2017, held in Vancouver, BC, Canada, in October 2017.The 12 full papers presented together with 1 short paper were carefully reviewed and selected from 43 submissions. This book also contains 3 keynote talks and 2 tutorials. The contributions included in this proceedings cover many aspects of theory and application of effective and efficient paradigms, approaches, and tools for building, maintaining, and managing secure and dependable systems and infrastructures, such as botnet detection, secure cloud computing and cryptosystems, IoT security, sensor and social network security, behavioral systems and data science, and mobile computing.