Authentication Technologies for Cloud Technology, IoT, and Big Data by Critical systems and infrastructure rely heavily on ICT systems and networks where security issues are a major concern. Authentication methods verify that messages come from trusted sources and guarantee the smooth flow of information and data. In this edited reference, the authors present state-of-art research and development in authentication technologies including challenges and applications for Cloud Technologies, IoT and Big Data. Topics covered include authentication; cryptographic algorithms; digital watermarking; biometric authentication; block ciphers with applications in IoT; identification schemes for Cloud and IoT; authentication issues for Cloud applications; cryptography engines for Cloud based on FPGA; and data protection laws.
Handbook of Research on Deep Learning Innovations and Trends by Leading technology firms and research institutions are continuously exploring new techniques in artificial intelligence and machine learning. As such, deep learning has now been recognized in various real-world applications such as computer vision, image processing, biometrics, pattern recognition, and medical imaging. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. The Handbook of Research on Deep Learning Innovations and Trends is an essential scholarly resource that presents current trends and the latest research on deep learning and explores the concepts, algorithms, and techniques of data mining and analysis. Highlighting topics such as computer vision, encryption systems, and biometrics, this book is ideal for researchers, practitioners, industry professionals, students, and academicians.
Machine Learning and Cognitive Science Applications in Cyber Security by In the past few years, with the evolution of advanced persistent threats and mutation techniques, sensitive and damaging information from a variety of sources have been exposed to possible corruption and hacking. Machine learning, artificial intelligence, predictive analytics, and similar disciplines of cognitive science applications have been found to have significant applications in the domain of cyber security. Machine Learning and Cognitive Science Applications in Cyber Security examines different applications of cognition that can be used to detect threats and analyze data to capture malware. Highlighting such topics as anomaly detection, intelligent platforms, and triangle scheme, this publication is designed for IT specialists, computer engineers, researchers, academicians, and industry professionals interested in the impact of machine learning in cyber security and the methodologies that can help improve the performance and reliability of machine learning applications.
Secure Cyber-Physical Systems for Smart Cities by Interest in developing smart cities has grown exponentially over the years with many governments across the world hoping to initiate these projects in their own countries. One of the key challenges for the success of any smart city project is the assurance of smart security and privacy of the citizens. Due to the use of a wide range of interconnected cyber-physical systems, traditional security solutions cannot be applied to smart city applications, and new practices must be sought. Secure Cyber-Physical Systems for Smart Cities is an essential reference publication that examines information security and privacy in smart city settings including discussions on new security frameworks, solutions, cybersecurity laws and regulations, and risk management frameworks for smart city environments. Covering a wide range of topics including wireless networks, security, and cyber-physical systems, this book is ideally designed for IT specialists and consultants, engineers, government officials, policymakers, researchers, academicians, and industry professionals.
Linking and Mining Heterogeneous and Multi-View Data by This book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an area of increasing significance with the pace of growth of data vastly outpacing any chance of labeling them manually. The book looks at the underlying algorithms and technologies that facilitate the area within big data analytics, it covers their applications across domains such as smarter transportation, social media, fake news detection and enterprise search among others. This book enables readers to understand a spectrum of advances in this emerging area, and it will hopefully empower them to leverage and develop methods in multi-source data fusion and analytics with applications to a variety of scenarios. Includes advances on unsupervised, semi-supervised and supervised approaches to heterogeneous data linkage and fusion; Covers use cases of analytics over multi-view and heterogeneous data from across a variety of domains such as fake news, smarter transportation and social media, among others; Provides a high-level overview of advances in this emerging field and empowers the reader to explore novel applications and methodologies that would enrich the field. 本學科指引提供您資訊科學工程研究領域的相關資源，整合館藏紙本、電子資源、期刊資源、學位論文、以及校外的網路資源等，主題式的引介各種資源使用。若需更進一步的協助，請與學科館員聯絡。
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