Foreword xv Preface xvii 1 Biometric Identification Using Deep Learning for Advance Cloud Security 1Navani Siroya and Manju Mandot 1.1 Introduction 2 1.2 Techniques of Biometric Identification 3 1.2.1 Fingerprint Identification 3 1.2.2 Iris Recognition 4 1.2.3 Facial Recognition 4 1.2.4 Voice Recognition 5 1.3 Approaches 6 1.3.1 Feature Selection 6 1.3.2 Feature Extraction 6 1.3.3 Face Marking 7 1.3.4 Nearest Neighbor Approach 8 1.4 Related Work, A Review 9 1.5 Proposed Work 10 1.6 Future Scope 12 1.7 Conclusion 12 References 12 2 Privacy in Multi-Tenancy Cloud Using Deep Learning 15Shweta Solanki and Prafull Narooka 2.1 Introduction 15 2.2 Basic Structure 16 2.2.1 Basic Structure of Cloud Computing 17 2.2.2 Concept of Multi-Tenancy 18 2.2.3 Concept of Multi-Tenancy with Cloud Computing 19 2.3 Privacy in Cloud Environment Using Deep Learning 21 2.4 Privacy in Multi-Tenancy with Deep Learning Concept 22 2.5 Related Work 23 2.6 Conclusion 24 References 25 3 Emotional Classification Using EEG Signals and Facial Expression: A Survey 27S J Savitha, Dr. M Paulraj and K Saranya 3.1 Introduction 27 3.2 Related Works 29 3.3 Methods 32 3.3.1 EEG Signal Pre-Processing 32 3.3.1.1 Discrete Fourier Transform (DFT) 32 3.3.1.2 Least Mean Square (LMS) Algorithm 32 3.3.1.3 Discrete Cosine Transform (DCT) 33 3.3.2 Feature Extraction Techniques 33 3.3.3 Classification Techniques 33 3.4 BCI Applications 34 3.4.1 Possible BCI Uses 36 3.4.2 Communication 36 3.4.3 Movement Control 36 3.4.4 Environment Control 37 3.4.5 Locomotion 38 3.5 Cloud-Based EEG Overview 38 3.5.1 Data Backup and Restoration 39 3.6 Conclusion 40 References 40 4 Effective and Efficient Wind Power Generation Using Bifarious Solar PV System 43R. Amirtha Katesa Sai Raj, M. Arun Kumar, S. Dinesh, U. Harisudhan and Dr. R. Uthirasamy 4.1 Introduction 44 4.2 Study of Bi-Facial Solar Panel 45 4.3 Proposed System 46 4.3.1 Block Diagram 46 4.3.2 DC Motor Mechanism 47 4.3.3 Battery Bank 48 4.3.4 System Management Using IoT 48 4.3.5 Structure of Proposed System 50 4.3.6 Spoiler Design 51 4.3.7 Working Principle of Proposed System 52 4.3.8 Design and Analysis 53 4.4 Applications of IoT in Renewable Energy Resources 53 4.4.1 Wind Turbine Reliability Using IoT 54 4.4.2 Siting of Wind Resource Using IoT 55 4.4.3 Application of Renewable Energy in Medical Industries 56 4.4.4 Data Analysis Using Deep Learning 57 4.5 Conclusion 59 References 59 5 Background Mosaicing Model for Wide Area Surveillance System 63Dr. E. Komagal 5.1 Introduction 64 5.2 Related Work 64 5.3 Methodology 65 5.3.1 Feature Extraction 66 5.3.2 Background Deep Learning Model Based on Mosaic 67 5.3.3 Foreground Segmentation 70 5.4 Results and Discussion 70 5.5 Conclusion 72 References 72 6 Prediction of CKD Stage 1 Using Three Different Classifiers 75Thamizharasan, K., Yamini, P., Shimola, A. and Sudha, S. 6.1 Introduction 75 6.2 Materials and Methods 78 6.3 Results and Discussion 84 6.4 Conclusions and Future Scope 89 References 89 7 Classification of MRI Images to Aid in Diagnosis of Neurological Disorder Using SVM 93Phavithra Selvaraj, Sruthi, M.S., Sridaran, M. and Dr. Jobin Christ M.C. 7.1 Introduction 93 7.2 Methodology 95 7.2.1 Data Acquisition 95 7.2.2 Image Preprocessing 96 7.2.3 Segmentation 97 7.2.4 Feature Extraction 98 7.2.5 Classification 99 7.3 Results and Discussions 100 7.3.1 Preprocessing 100 7.3.2 Classification 103 7.3.3 Validation 104 7.4 Conclusion 106 References 106 8 Convolutional Networks 109Simran Kaur and Rashmi Agrawal 8.1 Introduction 110 8.2 Convolution Operation 110 8.3 CNN 110 8.4 Practical Applications 112 8.4.1 Audio Data 112 8.4.2 Image Data 112 8.4.3 Text Data 113 8.5 Challenges of Profound Models 113 8.6 Deep Learning In Object Detection 114 8.7 CNN Architectures 114 8.8 Challenges of Item Location 118 8.8.1 Scale Variation Problem 118 8.8.2 Occlusion Problem 119 8.8.3 Deformation Problem 120 References 121 9 Categorization of Cloud Computing & Deep Learning 123Disha Shrmali 9.1 Introduction to Cloud Computing 123 9.1.1 Cloud Computing 123 9.1.2 Cloud Computing: History and Evolution 124 9.1.3 Working of Cloud 125 9.1.4 Characteristics of Cloud Computing 127 9.1.5 Different Types of Cloud Computing Service Models 128 9.1.5.1 Infrastructure as A Service (IAAS) 128 9.1.5.2 Platform as a Service (PAAS) 129 9.1.5.3 Software as a Service (SAAS) 129 9.1.6 Cloud Computing Advantages and Disadvantages 130 9.1.6.1 Advantages of Cloud Computing 130 9.1.6.2 Disadvantages of Cloud Computing 132 9.2 Introduction to Deep Learning 133 9.2.1 History and Revolution of Deep Learning 134 9.2.1.1 Development of Deep Learning Algorithms 134 9.2.1.2 The FORTRAN Code for Back Propagation 135 9.2.1.3 Deep Learning from the 2000s and Beyond 135 9.2.1.4 The Cat Experiment 136 9.2.2 Neural Networks 137 9.2.2.1 Artificial Neural Networks 137 9.2.2.2 Deep Neural Networks 138 9.2.3 Applications of Deep Learning 138 9.2.3.1 Automatic Speech Recognition 138 9.2.3.2 Electromyography (EMG) Recognition 139 9.2.3.3 Image Recognition 139 9.2.3.4 Visual Art Processing 140 9.2.3.5 Natural Language Processing 140 9.2.3.6 Drug Discovery and Toxicology 140 9.2.3.7 Customer Relationship Management 141 9.2.3.8 Recommendation Systems 141 9.2.3.9 Bioinformatics 141 9.2.3.10 Medical Image Analysis 141 9.2.3.11 Mobile Advertising 141 9.2.3.12 Image Restoration 142 9.2.3.13 Financial Fraud Detection 142 9.2.3.14 Military 142 9.3 Conclusion 142 References 143 10 Smart Load Balancing in Cloud Using Deep Learning 145Astha Parihar and Shweta Sharma 10.1 Introduction 146 10.2 Load Balancing 147 10.2.1 Static Algorithm 148 10.2.2 Dynamic (Run-Time) Algorithms 148 10.3 Load Adjusting in Distributing Computing 149 10.3.1 Working of Load Balancing 151 10.4 Cloud Load Balancing Criteria (Measures) 152 10.5 Load Balancing Proposed for Cloud Computing 153 10.5.1 Calculation of Load Balancing in the Whole System 154 10.6 Load Balancing in Next Generation Cloud Computing 155 10.7 Dispersed AI Load Adjusting Methodology in Distributed Computing Administrations 157 10.7.1 Quantum Isochronous Parallel 158 10.7.2 Phase Isochronous Parallel 159 10.7.3 Dynamic Isochronous Coordinate Strategy 161 10.8 Adaptive-Dynamic Synchronous Coordinate Strategy 161 10.8.1 Adaptive Quick Reassignment (AdaptQR) 162 10.8.2 A-DIC (Adaptive-Dynamic Synchronous Parallel) 163 10.9 Conclusion 164 References 165 11 Biometric Identification for Advanced Cloud Security 167Yojna khandelwal and Kapil Chauhan 11.1 Introduction 168 11.1.1 Biometric Identification 168 11.1.2 Biometric Characteristic 169 11.1.3 Types of Biometric Data 169 11.1.3.1 Face Recognition 169 11.1.3.2 Hand Vein 170 11.1.3.3 Signature Verification 170 11.1.3.4 Iris Recognition 170 11.1.3.5 Voice Recognition 170 11.1.3.6 Fingerprints 171 11.2 Literature Survey 172 11.3 Biometric Identification in Cloud Computing 174 11.3.1 How Biometric Authentication is Being Used on the Cloud Platform 176 11.4 Models and Design Goals 177 11.4.1 Models 177 11.4.1.1 System Model 177 11.4.1.2 Threat Model 177 11.4.2 Design Goals 178 11.5 Face Recognition Method as a Biometric Authentication 179 11.6 Deep Learning Techniques for Big Data in Biometrics 180 11.6.1 Issues and Challenges 181 11.6.2 Deep Learning Strategies For Biometric Identification 182 11.7 Conclusion 185 References 185 12 Application of Deep Learning in Cloud Security 189Jaya Jain 12.1 Introduction 190 12.2 Literature Review 191 12.3 Deep Learning 192 12.4 The Uses of Fields in Deep Learning 195 12.5 Conclusion 202 References 203 13 Real Time Cloud Based Intrusion Detection 207Ekta Bafna 13.1 Introduction 207 13.2 Literature Review 209 13.3 Incursion In Cloud 211 13.3.1 Denial of Service (DoS) Attack 212 13.3.2 Insider Attack 212 13.3.3 User To Root (U2R) Attack 213 13.3.4 Port Scanning 213 13.4 Intrusion Detection System 213 13.4.1 Signature-Based Intrusion Detection System (SIDS) 213 13.4.2 Anomaly-Based Intrusion Detection System (AIDS) 214 13.4.3 Intrusion Detection System Using Deep Learning 215 13.5 Types of IDS in Cloud 216 13.5.1 Host Intrusion Detection System 216 13.5.2 Network Based Intrusion Detection System 217 13.5.3 Distributed Based Intrusion Detection System 217 13.6 Model of Deep Learning 218 13.6.1 ConvNet Model 218 13.6.2 Recurrent Neural Network 219 13.6.3 Multi-Layer Perception Model 219 13.7 KDD Dataset 221 13.8 Evaluation 221 13.9 Conclusion 223 References 223 14 Applications of Deep Learning in Cloud Security 225Disha Shrmali and Shweta Sharma 14.1 Introduction 226 14.1.1 Data Breaches 226 14.1.2 Accounts Hijacking 227 14.1.3 Insider Threat 227 14.1.3.1 Malware Injection 227 14.1.3.2 Abuse of Cloud Services 228 14.1.3.3 Insecure APIs 228 14.1.3.4 Denial of Service Attacks 228 14.1.3.5 Insufficient Due Diligence 229 14.1.3.6 Shared Vulnerabilities 229 14.1.3.7 Data Loss 229 14.2 Deep Learning Methods for Cloud Cyber Security 230 14.2.1 Deep Belief Networks 230 14.2.1.1 Deep Autoencoders 230 14.2.1.2 Restricted Boltzmann Machines 232 14.2.1.3 DBNs, RBMs, or Deep Autoencoders Coupled with Classification Layers 233 14.2.1.4 Recurrent Neural Networks 233 14.2.1.5 Convolutional Neural Networks 234 14.2.1.6 Generative Adversarial Networks 235 14.2.1.7 Recursive Neural Networks 236 14.2.2 Applications of Deep Learning in Cyber Security 237 14.2.2.1 Intrusion Detection and Prevention Systems (IDS/IPS) 237 14.2.2.2 Dealing with Malware 237 14.2.2.3 Spam and Social Engineering Detection 238 14.2.2.4 Network Traffic Analysis 238 14.2.2.5 User Behaviour Analytics 238 14.2.2.6 Insider Threat Detection 239 14.2.2.7 Border Gateway Protocol Anomaly Detection 239 14.2.2.8 Verification if Keystrokes were Typed by a Human 240 14.3 Framework to Improve Security in Cloud Computing 240 14.3.1 Introduction to Firewalls 241 14.3.2 Importance of Firewalls 242 14.3.2.1 Prevents the Passage of Unwanted Content 242 14.3.2.2 Prevents Unauthorized Remote Access 243 14.3.2.3 Restrict Indecent Content 243 14.3.2.4 Guarantees Security Based on Protocol and IP Address 244 14.3.2.5 Protects Seamless Operations in Enterprises 244 14.3.2.6 Protects Conversations and Coordination Contents 244 14.3.2.7 Restricts Online Videos and Games from Displaying Destructive Content 245 14.3.3 Types of Firewalls 245 14.3.3.1 Proxy-Based Firewalls 245 14.3.3.2 Stateful Firewalls 246 14.3.3.3 Next-Generation Firewalls (NGF) 247 14.3.3.4 Web Application Firewalls (WAF) 247 14.3.3.5 Working of WAF 248 14.3.3.6 How Web Application Firewalls (WAF) Work 248 14.3.3.7 Attacks that Web Application Firewalls Prevent 250 14.3.3.8 Cloud WAF 251 14.4 WAF Deployment 251 14.4.1 Web Application Firewall (WAF) Security Models 252 14.4.2 Firewall-as-a-Service (FWaaS) 252 14.4.3 Basic Difference Between a Cloud Firewall and a Next-Generation Firewall (NGFW) 253 14.4.4 Introduction and Effects of Firewall Network Parameters on Cloud Computing 253 14.5 Conclusion 254 References 254 About the Editors 257 Index 263 

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 securityIs 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 areaDiscusses 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