Deep Learning Approaches to Cloud Security: Deep Learning Approaches for Cloud Security
- Length: 304 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2022-01-26
- ISBN-10: 1119760526
- ISBN-13: 9781119760528
- Sales Rank: #0 (See Top 100 Books)
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
Cover Table of Contents Title Page Copyright Foreword Preface 1 Biometric Identification Using Deep Learning for Advance Cloud Security 1.1 Introduction 1.2 Techniques of Biometric Identification 1.3 Approaches 1.4 Related Work, A Review 1.5 Proposed Work 1.6 Future Scope 1.7 Conclusion References 2 Privacy in Multi-Tenancy Cloud Using Deep Learning 2.1 Introduction 2.2 Basic Structure 2.3 Privacy in Cloud Environment Using Deep Learning 2.4 Privacy in Multi-Tenancy with Deep Learning Concept 2.5 Related Work 2.6 Conclusion References 3 Emotional Classification Using EEG Signals and Facial Expression: A Survey 3.1 Introduction 3.2 Related Works 3.3 Methods 3.4 BCI Applications 3.5 Cloud-Based EEG Overview 3.6 Conclusion References 4 Effective and Efficient Wind Power Generation Using Bifarious Solar PV System 4.1 Introduction 4.2 Study of Bi-Facial Solar Panel 4.3 Proposed System 4.4 Applications of IoT in Renewable Energy Resources 4.5 Conclusion References 5 Background Mosaicing Model for Wide Area Surveillance System 5.1 Introduction 5.2 Related Work 5.3 Methodology 5.4 Results and Discussion 5.5 Conclusion References 6 Prediction of CKD Stage 1 Using Three Different Classifiers 6.1 Introduction 6.2 Materials and Methods 6.3 Results and Discussion 6.4 Conclusions and Future Scope References 7 Classification of MRI Images to Aid in Diagnosis of Neurological Disorder Using SVM 7.1 Introduction 7.2 Methodology 7.3 Results and Discussions 7.4 Conclusion References 8 Convolutional Networks 8.1 Introduction 8.2 Convolution Operation 8.3 CNN 8.4 Practical Applications 8.5 Challenges of Profound Models 8.6 Deep Learning In Object Detection 8.7 CNN Architectures 8.8 Challenges of Item Location References 9 Categorization of Cloud Computing & Deep Learning 9.1 Introduction to Cloud Computing 9.2 Introduction to Deep Learning 9.3 Conclusion References 10 Smart Load Balancing in Cloud Using Deep Learning 10.1 Introduction 10.2 Load Balancing 10.3 Load Adjusting in Distributing Computing 10.4 Cloud Load Balancing Criteria (Measures) 10.5 Load Balancing Proposed for Cloud Computing 10.6 Load Balancing in Next Generation Cloud Computing 10.7 Dispersed AI Load Adjusting Methodology in Distributed Computing Administrations 10.8 Adaptive-Dynamic Synchronous Coordinate Strategy 10.9 Conclusion References 11 Biometric Identification for Advanced Cloud Security 11.1 Introduction 11.2 Literature Survey 11.3 Biometric Identification in Cloud Computing 11.4 Models and Design Goals 11.5 Face Recognition Method as a Biometric Authentication 11.6 Deep Learning Techniques for Big Data in Biometrics 11.7 Conclusion References 12 Application of Deep Learning in Cloud Security 12.1 Introduction 12.2 Literature Review 12.3 Deep Learning 12.4 The Uses of Fields in Deep Learning 12.5 Conclusion References 13 Real Time Cloud Based Intrusion Detection 13.1 Introduction 13.2 Literature Review 13.3 Incursion In Cloud 13.4 Intrusion Detection System 13.5 Types of IDS in Cloud 13.6 Model of Deep Learning 13.7 KDD Dataset 13.8 Evaluation 13.9 Conclusion References 14 Applications of Deep Learning in Cloud Security 14.1 Introduction 14.2 Deep Learning Methods for Cloud Cyber Security 14.3 Framework to Improve Security in Cloud Computing 14.4 WAF Deployment 14.5 Conclusion References About the Editors Index End User License Agreement
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