Deep Learning Approaches To Cloud Security
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Deep Learning Approaches To Cloud Security
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Author : Pramod Singh Rathore
language : en
Publisher: John Wiley & Sons
Release Date : 2022-01-26
Deep Learning Approaches To Cloud Security written by Pramod Singh Rathore and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-26 with Technology & Engineering categories.
DEEP LEARNING APPROACHES TO CLOUD SECURITY Covering one of the most important subjects to our society today, cloud security, this editorial team delves into solutions taken from evolving deep learning approaches, solutions allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. Deep learning is the fastest growing field in computer science. Deep learning algorithms and techniques are found to be useful in different areas like automatic machine translation, automatic handwriting generation, visual recognition, fraud detection, and detecting developmental delay in children. However, applying deep learning techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. This book provides state of the art approaches of deep learning in these areas, including areas of detection and prediction, as well as future framework development, building service systems and analytical aspects. In all these topics, deep learning approaches, such as artificial neural networks, fuzzy logic, genetic algorithms, and hybrid mechanisms are used. This book is intended for dealing with modeling and performance prediction of the efficient cloud security systems, thereby bringing a newer dimension to this rapidly evolving field. This groundbreaking new volume presents these topics and trends of deep learning, bridging the research gap, and presenting solutions to the challenges facing the engineer or scientist every day in this area. Whether for the veteran engineer or the student, this is a must-have for any library. Deep Learning Approaches to Cloud Security: Is the first volume of its kind to go in-depth on the newest trends and innovations in cloud security through the use of deep learning approaches Covers these important new innovations, such as AI, data mining, and other evolving computing technologies in relation to cloud security Is a useful reference for the veteran computer scientist or engineer working in this area or an engineer new to the area, or a student in this area Discusses not just the practical applications of these technologies, but also the broader concepts and theory behind how these deep learning tools are vital not just to cloud security, but society as a whole Audience: Computer scientists, scientists and engineers working with information technology, design, network security, and manufacturing, researchers in computers, electronics, and electrical and network security, integrated domain, and data analytics, and students in these areas
Machine Learning Techniques And Analytics For Cloud Security
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Author : Rajdeep Chakraborty
language : en
Publisher: John Wiley & Sons
Release Date : 2021-11-30
Machine Learning Techniques And Analytics For Cloud Security written by Rajdeep Chakraborty and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-30 with Computers categories.
MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.
Privacy Preserving Deep Learning
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Author : Kwangjo Kim
language : en
Publisher: Springer Nature
Release Date : 2021-07-22
Privacy Preserving Deep Learning written by Kwangjo Kim and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-22 with Computers categories.
This book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocols with deep learning. Google and Microsoft announced a major investment in PPDL in early 2019. This was followed by Google’s infamous announcement of “Private Join and Compute,” an open source PPDL tools based on secure multi-party computation (secure MPC) and homomorphic encryption (HE) in June of that year. One of the challenging issues concerning PPDL is selecting its practical applicability despite the gap between the theory and practice. In order to solve this problem, it has recently been proposed that in addition to classical privacy-preserving methods (HE, secure MPC, differential privacy, secure enclaves), new federated or split learning for PPDL should also be applied. This concept involves building a cloud framework that enables collaborative learning while keeping training data on client devices. This successfully preserves privacy and while allowing the framework to be implemented in the real world. This book provides fundamental insights into privacy-preserving and deep learning, offering a comprehensive overview of the state-of-the-art in PPDL methods. It discusses practical issues, and leveraging federated or split-learning-based PPDL. Covering the fundamental theory of PPDL, the pros and cons of current PPDL methods, and addressing the gap between theory and practice in the most recent approaches, it is a valuable reference resource for a general audience, undergraduate and graduate students, as well as practitioners interested learning about PPDL from the scratch, and researchers wanting to explore PPDL for their applications.
Security And Risk Analysis For Intelligent Cloud Computing
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Author : Ajay Kumar
language : en
Publisher: CRC Press
Release Date : 2023-12-19
Security And Risk Analysis For Intelligent Cloud Computing written by Ajay Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-19 with Computers categories.
This edited book is a compilation of scholarly articles on the latest developments in the field of AI, Blockchain, and ML/DL in cloud security. This book is designed for security and risk assessment professionals, and to help undergraduate, postgraduate students, research scholars, academicians, and technology professionals who are interested in learning practical approaches to cloud security. It covers practical strategies for assessing the security and privacy of cloud infrastructure and applications and shows how to make cloud infrastructure secure to combat threats and attacks, and prevent data breaches. The chapters are designed with a granular framework, starting with the security concepts, followed by hands-on assessment techniques based on real-world studies. Readers will gain detailed information on cloud computing security that—until now—has been difficult to access. This book: • Covers topics such as AI, Blockchain, and ML/DL in cloud security. • Presents several case studies revealing how threat actors abuse and exploit cloud environments to spread threats. • Explains the privacy aspects you need to consider in the cloud, including how they compare with aspects considered in traditional computing models. • Examines security delivered as a service—a different facet of cloud security.
Artificial Intelligence In Cyber Security Theories And Applications
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Author : Tushar Bhardwaj
language : en
Publisher: Springer Nature
Release Date : 2023-10-06
Artificial Intelligence In Cyber Security Theories And Applications written by Tushar Bhardwaj and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-06 with Technology & Engineering categories.
This book highlights the applications and theory of artificial intelligence in the domain of cybersecurity. The book proposes new approaches and ideas to present applications of innovative approaches in real-time environments. In the past few decades, there has been an exponential rise in the application of artificial intelligence technologies (such as deep learning, machine learning, blockchain) for solving complex and intricate problems arising in the domain of cybersecurity. The versatility of these techniques has made them a favorite among scientists and researchers working in diverse areas. This book serves as a reference for young scholars, researchers, and industry professionals working in the field of Artificial Intelligence and Cybersecurity.
Deep Learning Approaches For Security Threats In Iot Environments
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Author : Mohamed Abdel-Basset
language : en
Publisher: John Wiley & Sons
Release Date : 2022-12-08
Deep Learning Approaches For Security Threats In Iot Environments written by Mohamed Abdel-Basset and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-08 with Computers categories.
Deep Learning Approaches for Security Threats in IoT Environments An expert discussion of the application of deep learning methods in the IoT security environment In Deep Learning Approaches for Security Threats in IoT Environments, a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation. This book applies deep learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues. Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They’ll also find: A thorough introduction to artificial intelligence and the Internet of Things, including key concepts like deep learning, security, and privacy Comprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networks In-depth examinations of the architectural design of cloud, fog, and edge computing networks Fulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networks Perfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks.
Deep Learning Techniques For Iot Security And Privacy
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Author : Mohamed Abdel-Basset
language : en
Publisher: Springer Nature
Release Date : 2021-12-05
Deep Learning Techniques For Iot Security And Privacy written by Mohamed Abdel-Basset and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-05 with Computers categories.
This book states that the major aim audience are people who have some familiarity with Internet of things (IoT) but interested to get a comprehensive interpretation of the role of deep Learning in maintaining the security and privacy of IoT. A reader should be friendly with Python and the basics of machine learning and deep learning. Interpretation of statistics and probability theory will be a plus but is not certainly vital for identifying most of the book's material.
Machine Learning Approach For Cloud Data Analytics In Iot
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Author : Sachi Nandan Mohanty
language : en
Publisher: John Wiley & Sons
Release Date : 2021-07-27
Machine Learning Approach For Cloud Data Analytics In Iot written by Sachi Nandan Mohanty and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-27 with Computers categories.
Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.
Ccsp Certified Cloud Security Professional All In One Exam Guide Third Edition
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Author : Daniel Carter
language : en
Publisher: McGraw Hill Professional
Release Date : 2022-11-25
Ccsp Certified Cloud Security Professional All In One Exam Guide Third Edition written by Daniel Carter and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-25 with Computers categories.
This fully updated self-study guide delivers 100% coverage of all topics on the current version of the CCSP exam Thoroughly revised for the 2022 edition of the exam, this highly effective test preparation guide covers all six domains within the CCSP Body of Knowledge. The book offers clear explanations of every subject on the CCSP exam and features accurate practice questions and real-world examples. New, updated, or expanded coverage includes cloud data security, DevOps security, mobile computing, threat modeling paradigms, regulatory and legal frameworks, and best practices and standards. Written by a respected computer security expert, CCSP Certified Cloud Security Professional All-in-One Exam Guide, Third Edition is both a powerful study tool and a valuable reference that will serve professionals long after the test. To aid in self-study, each chapter includes exam tips that highlight key information, a summary that serves as a quick review of salient points, and practice questions that allow you to test your comprehension. Special design elements throughout provide insight and call out potentially harmful situations. All practice questions match the tone, content, and format of those on the actual exam Includes access to 300 practice questions in the TotalTesterTM Online customizable test engine Written by an IT security expert and experienced author
Enhancing Steganography Through Deep Learning Approaches
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Author : Kumar, Vijay
language : en
Publisher: IGI Global
Release Date : 2024-11-04
Enhancing Steganography Through Deep Learning Approaches written by Kumar, Vijay and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-04 with Computers categories.
In an era defined by digital connectivity, securing sensitive information against cyber threats is a pressing concern. As digital transmission systems advance, so do the methods of intrusion and data theft. Traditional security measures often need to catch up in safeguarding against sophisticated cyber-attacks. This book presents a timely solution by integrating steganography, the ancient art of concealing information, with cutting-edge deep learning techniques. By blending these two technologies, the book offers a comprehensive approach to fortifying the security of digital communication channels. Enhancing Steganography Through Deep Learning Approaches addresses critical issues in national information security, business and personal privacy, property security, counterterrorism, and internet security. It thoroughly explores steganography's application in bolstering security across various domains. Readers will gain insights into the fusion of deep learning and steganography for advanced encryption and data protection, along with innovative steganographic techniques for securing physical and intellectual property. The book also delves into real-world examples of thwarting malicious activities using deep learning-enhanced steganography. This book is tailored for academics and researchers in Artificial Intelligence, postgraduate students seeking in-depth knowledge in AI and deep learning, smart computing practitioners, data analysis professionals, and security sector professionals.