{"product_id":"deep-learning-approaches-to-cloud-security-hardback-9781119760528","title":"Deep Learning Approaches to Cloud Security (Hardback) 9781119760528","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eDeep Learning Approaches to Cloud Security\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003ePramod Singh Rathore (Edited by), PS Rathore (Author), Vishal Dutt (Edited by), Rashmi Agrawal (Edited by), Satya Murthy Sasubilli (Edited by), Srinivasa Rao Swarna (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119760528, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 25 January 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e304 pages\u003cbr\u003e1 x 1 x 1 cm, 0.454 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eDEEP LEARNING APPROACHES TO CLOUD SECURITY\u003c\/b\u003e \u003cp\u003e\u003cb\u003eCovering 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eDeep 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. \u003c\/p\u003e\n\u003cp\u003eThis 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.  \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e\u003ci\u003eDeep Learning Approaches to Cloud Security:\u003c\/i\u003e\u003c\/b\u003e \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIs 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 \u003c\/li\u003e \u003cli\u003eCovers these important new innovations, such as AI, data mining, and other evolving computing technologies in relation to cloud security\u003c\/li\u003e \u003cli\u003eIs 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\u003c\/li\u003e \u003cli\u003eDiscusses 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 \u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience:\u003c\/b\u003e 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\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eForeword xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Biometric Identification Using Deep Learning for Advance Cloud Security 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNavani Siroya and Manju Mandot\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Techniques of Biometric Identification 3\u003c\/p\u003e \u003cp\u003e1.2.1 Fingerprint Identification 3\u003c\/p\u003e \u003cp\u003e1.2.2 Iris Recognition 4\u003c\/p\u003e \u003cp\u003e1.2.3 Facial Recognition 4\u003c\/p\u003e \u003cp\u003e1.2.4 Voice Recognition 5\u003c\/p\u003e \u003cp\u003e1.3 Approaches 6\u003c\/p\u003e \u003cp\u003e1.3.1 Feature Selection 6\u003c\/p\u003e \u003cp\u003e1.3.2 Feature Extraction 6\u003c\/p\u003e \u003cp\u003e1.3.3 Face Marking 7\u003c\/p\u003e \u003cp\u003e1.3.4 Nearest Neighbor Approach 8\u003c\/p\u003e \u003cp\u003e1.4 Related Work, A Review 9\u003c\/p\u003e \u003cp\u003e1.5 Proposed Work 10\u003c\/p\u003e \u003cp\u003e1.6 Future Scope 12\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 12\u003c\/p\u003e \u003cp\u003eReferences 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Privacy in Multi-Tenancy Cloud Using Deep Learning 15\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShweta Solanki and Prafull Narooka\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 15\u003c\/p\u003e \u003cp\u003e2.2 Basic Structure 16\u003c\/p\u003e \u003cp\u003e2.2.1 Basic Structure of Cloud Computing 17\u003c\/p\u003e \u003cp\u003e2.2.2 Concept of Multi-Tenancy 18\u003c\/p\u003e \u003cp\u003e2.2.3 Concept of Multi-Tenancy with Cloud Computing 19\u003c\/p\u003e \u003cp\u003e2.3 Privacy in Cloud Environment Using Deep Learning 21\u003c\/p\u003e \u003cp\u003e2.4 Privacy in Multi-Tenancy with Deep Learning Concept 22\u003c\/p\u003e \u003cp\u003e2.5 Related Work 23\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 24\u003c\/p\u003e \u003cp\u003eReferences 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Emotional Classification Using EEG Signals and Facial Expression: A Survey 27\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eS J Savitha, Dr. M Paulraj and K Saranya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 27\u003c\/p\u003e \u003cp\u003e3.2 Related Works 29\u003c\/p\u003e \u003cp\u003e3.3 Methods 32\u003c\/p\u003e \u003cp\u003e3.3.1 EEG Signal Pre-Processing 32\u003c\/p\u003e \u003cp\u003e3.3.1.1 Discrete Fourier Transform (DFT) 32\u003c\/p\u003e \u003cp\u003e3.3.1.2 Least Mean Square (LMS) Algorithm 32\u003c\/p\u003e \u003cp\u003e3.3.1.3 Discrete Cosine Transform (DCT) 33\u003c\/p\u003e \u003cp\u003e3.3.2 Feature Extraction Techniques 33\u003c\/p\u003e \u003cp\u003e3.3.3 Classification Techniques 33\u003c\/p\u003e \u003cp\u003e3.4 BCI Applications 34\u003c\/p\u003e \u003cp\u003e3.4.1 Possible BCI Uses 36\u003c\/p\u003e \u003cp\u003e3.4.2 Communication 36\u003c\/p\u003e \u003cp\u003e3.4.3 Movement Control 36\u003c\/p\u003e \u003cp\u003e3.4.4 Environment Control 37\u003c\/p\u003e \u003cp\u003e3.4.5 Locomotion 38\u003c\/p\u003e \u003cp\u003e3.5 Cloud-Based EEG Overview 38\u003c\/p\u003e \u003cp\u003e3.5.1 Data Backup and Restoration 39\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 40\u003c\/p\u003e \u003cp\u003eReferences 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Effective and Efficient Wind Power Generation Using Bifarious Solar PV System 43\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eR. Amirtha Katesa Sai Raj, M. Arun Kumar, S. Dinesh, U. Harisudhan and Dr. R. Uthirasamy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 44\u003c\/p\u003e \u003cp\u003e4.2 Study of Bi-Facial Solar Panel 45\u003c\/p\u003e \u003cp\u003e4.3 Proposed System 46\u003c\/p\u003e \u003cp\u003e4.3.1 Block Diagram 46\u003c\/p\u003e \u003cp\u003e4.3.2 DC Motor Mechanism 47\u003c\/p\u003e \u003cp\u003e4.3.3 Battery Bank 48\u003c\/p\u003e \u003cp\u003e4.3.4 System Management Using IoT 48\u003c\/p\u003e \u003cp\u003e4.3.5 Structure of Proposed System 50\u003c\/p\u003e \u003cp\u003e4.3.6 Spoiler Design 51\u003c\/p\u003e \u003cp\u003e4.3.7 Working Principle of Proposed System 52\u003c\/p\u003e \u003cp\u003e4.3.8 Design and Analysis 53\u003c\/p\u003e \u003cp\u003e4.4 Applications of IoT in Renewable Energy Resources 53\u003c\/p\u003e \u003cp\u003e4.4.1 Wind Turbine Reliability Using IoT 54\u003c\/p\u003e \u003cp\u003e4.4.2 Siting of Wind Resource Using IoT 55\u003c\/p\u003e \u003cp\u003e4.4.3 Application of Renewable Energy in Medical Industries 56\u003c\/p\u003e \u003cp\u003e4.4.4 Data Analysis Using Deep Learning 57\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 59\u003c\/p\u003e \u003cp\u003eReferences 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Background Mosaicing Model for Wide Area Surveillance System 63\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDr. E. Komagal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 64\u003c\/p\u003e \u003cp\u003e5.2 Related Work 64\u003c\/p\u003e \u003cp\u003e5.3 Methodology 65\u003c\/p\u003e \u003cp\u003e5.3.1 Feature Extraction 66\u003c\/p\u003e \u003cp\u003e5.3.2 Background Deep Learning Model Based on Mosaic 67\u003c\/p\u003e \u003cp\u003e5.3.3 Foreground Segmentation 70\u003c\/p\u003e \u003cp\u003e5.4 Results and Discussion 70\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 72\u003c\/p\u003e \u003cp\u003eReferences 72\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Prediction of CKD Stage 1 Using Three Different Classifiers 75\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eThamizharasan, K., Yamini, P., Shimola, A. and Sudha, S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 75\u003c\/p\u003e \u003cp\u003e6.2 Materials and Methods 78\u003c\/p\u003e \u003cp\u003e6.3 Results and Discussion 84\u003c\/p\u003e \u003cp\u003e6.4 Conclusions and Future Scope 89\u003c\/p\u003e \u003cp\u003eReferences 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Classification of MRI Images to Aid in Diagnosis of Neurological Disorder Using SVM 93\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePhavithra Selvaraj, Sruthi, M.S., Sridaran, M. and Dr. Jobin Christ M.C.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 93\u003c\/p\u003e \u003cp\u003e7.2 Methodology 95\u003c\/p\u003e \u003cp\u003e7.2.1 Data Acquisition 95\u003c\/p\u003e \u003cp\u003e7.2.2 Image Preprocessing 96\u003c\/p\u003e \u003cp\u003e7.2.3 Segmentation 97\u003c\/p\u003e \u003cp\u003e7.2.4 Feature Extraction 98\u003c\/p\u003e \u003cp\u003e7.2.5 Classification 99\u003c\/p\u003e \u003cp\u003e7.3 Results and Discussions 100\u003c\/p\u003e \u003cp\u003e7.3.1 Preprocessing 100\u003c\/p\u003e \u003cp\u003e7.3.2 Classification 103\u003c\/p\u003e \u003cp\u003e7.3.3 Validation 104\u003c\/p\u003e \u003cp\u003e7.4 Conclusion 106\u003c\/p\u003e \u003cp\u003eReferences 106\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Convolutional Networks 109\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSimran Kaur and Rashmi Agrawal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 110\u003c\/p\u003e \u003cp\u003e8.2 Convolution Operation 110\u003c\/p\u003e \u003cp\u003e8.3 CNN 110\u003c\/p\u003e \u003cp\u003e8.4 Practical Applications 112\u003c\/p\u003e \u003cp\u003e8.4.1 Audio Data 112\u003c\/p\u003e \u003cp\u003e8.4.2 Image Data 112\u003c\/p\u003e \u003cp\u003e8.4.3 Text Data 113\u003c\/p\u003e \u003cp\u003e8.5 Challenges of Profound Models 113\u003c\/p\u003e \u003cp\u003e8.6 Deep Learning In Object Detection 114\u003c\/p\u003e \u003cp\u003e8.7 CNN Architectures 114\u003c\/p\u003e \u003cp\u003e8.8 Challenges of Item Location 118\u003c\/p\u003e \u003cp\u003e8.8.1 Scale Variation Problem 118\u003c\/p\u003e \u003cp\u003e8.8.2 Occlusion Problem 119\u003c\/p\u003e \u003cp\u003e8.8.3 Deformation Problem 120\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Categorization of Cloud Computing \u0026amp; Deep Learning 123\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDisha Shrmali\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction to Cloud Computing 123\u003c\/p\u003e \u003cp\u003e9.1.1 Cloud Computing 123\u003c\/p\u003e \u003cp\u003e9.1.2 Cloud Computing: History and Evolution 124\u003c\/p\u003e \u003cp\u003e9.1.3 Working of Cloud 125\u003c\/p\u003e \u003cp\u003e9.1.4 Characteristics of Cloud Computing 127\u003c\/p\u003e \u003cp\u003e9.1.5 Different Types of Cloud Computing Service Models 128\u003c\/p\u003e \u003cp\u003e9.1.5.1 Infrastructure as A Service (IAAS) 128\u003c\/p\u003e \u003cp\u003e9.1.5.2 Platform as a Service (PAAS) 129\u003c\/p\u003e \u003cp\u003e9.1.5.3 Software as a Service (SAAS) 129\u003c\/p\u003e \u003cp\u003e9.1.6 Cloud Computing Advantages and Disadvantages 130\u003c\/p\u003e \u003cp\u003e9.1.6.1 Advantages of Cloud Computing 130\u003c\/p\u003e \u003cp\u003e9.1.6.2 Disadvantages of Cloud Computing 132\u003c\/p\u003e \u003cp\u003e9.2 Introduction to Deep Learning 133\u003c\/p\u003e \u003cp\u003e9.2.1 History and Revolution of Deep Learning 134\u003c\/p\u003e \u003cp\u003e9.2.1.1 Development of Deep Learning Algorithms 134\u003c\/p\u003e \u003cp\u003e9.2.1.2 The FORTRAN Code for Back Propagation 135\u003c\/p\u003e \u003cp\u003e9.2.1.3 Deep Learning from the 2000s and Beyond 135\u003c\/p\u003e \u003cp\u003e9.2.1.4 The Cat Experiment 136\u003c\/p\u003e \u003cp\u003e9.2.2 Neural Networks 137\u003c\/p\u003e \u003cp\u003e9.2.2.1 Artificial Neural Networks 137\u003c\/p\u003e \u003cp\u003e9.2.2.2 Deep Neural Networks 138\u003c\/p\u003e \u003cp\u003e9.2.3 Applications of Deep Learning 138\u003c\/p\u003e \u003cp\u003e9.2.3.1 Automatic Speech Recognition 138\u003c\/p\u003e \u003cp\u003e9.2.3.2 Electromyography (EMG) Recognition 139\u003c\/p\u003e \u003cp\u003e9.2.3.3 Image Recognition 139\u003c\/p\u003e \u003cp\u003e9.2.3.4 Visual Art Processing 140\u003c\/p\u003e \u003cp\u003e9.2.3.5 Natural Language Processing 140\u003c\/p\u003e \u003cp\u003e9.2.3.6 Drug Discovery and Toxicology 140\u003c\/p\u003e \u003cp\u003e9.2.3.7 Customer Relationship Management 141\u003c\/p\u003e \u003cp\u003e9.2.3.8 Recommendation Systems 141\u003c\/p\u003e \u003cp\u003e9.2.3.9 Bioinformatics 141\u003c\/p\u003e \u003cp\u003e9.2.3.10 Medical Image Analysis 141\u003c\/p\u003e \u003cp\u003e9.2.3.11 Mobile Advertising 141\u003c\/p\u003e \u003cp\u003e9.2.3.12 Image Restoration 142\u003c\/p\u003e \u003cp\u003e9.2.3.13 Financial Fraud Detection 142\u003c\/p\u003e \u003cp\u003e9.2.3.14 Military 142\u003c\/p\u003e \u003cp\u003e9.3 Conclusion 142\u003c\/p\u003e \u003cp\u003eReferences 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Smart Load Balancing in Cloud Using Deep Learning 145\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAstha Parihar and Shweta Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 146\u003c\/p\u003e \u003cp\u003e10.2 Load Balancing 147\u003c\/p\u003e \u003cp\u003e10.2.1 Static Algorithm 148\u003c\/p\u003e \u003cp\u003e10.2.2 Dynamic (Run-Time) Algorithms 148\u003c\/p\u003e \u003cp\u003e10.3 Load Adjusting in Distributing Computing 149\u003c\/p\u003e \u003cp\u003e10.3.1 Working of Load Balancing 151\u003c\/p\u003e \u003cp\u003e10.4 Cloud Load Balancing Criteria (Measures) 152\u003c\/p\u003e \u003cp\u003e10.5 Load Balancing Proposed for Cloud Computing 153\u003c\/p\u003e \u003cp\u003e10.5.1 Calculation of Load Balancing in the Whole System 154\u003c\/p\u003e \u003cp\u003e10.6 Load Balancing in Next Generation Cloud Computing 155\u003c\/p\u003e \u003cp\u003e10.7 Dispersed AI Load Adjusting Methodology in Distributed Computing Administrations 157\u003c\/p\u003e \u003cp\u003e10.7.1 Quantum Isochronous Parallel 158\u003c\/p\u003e \u003cp\u003e10.7.2 Phase Isochronous Parallel 159\u003c\/p\u003e \u003cp\u003e10.7.3 Dynamic Isochronous Coordinate Strategy 161\u003c\/p\u003e \u003cp\u003e10.8 Adaptive-Dynamic Synchronous Coordinate Strategy 161\u003c\/p\u003e \u003cp\u003e10.8.1 Adaptive Quick Reassignment (AdaptQR) 162\u003c\/p\u003e \u003cp\u003e10.8.2 A-DIC (Adaptive-Dynamic Synchronous Parallel) 163\u003c\/p\u003e \u003cp\u003e10.9 Conclusion 164\u003c\/p\u003e \u003cp\u003eReferences 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Biometric Identification for Advanced Cloud Security 167\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eYojna khandelwal and Kapil Chauhan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 168\u003c\/p\u003e \u003cp\u003e11.1.1 Biometric Identification 168\u003c\/p\u003e \u003cp\u003e11.1.2 Biometric Characteristic 169\u003c\/p\u003e \u003cp\u003e11.1.3 Types of Biometric Data 169\u003c\/p\u003e \u003cp\u003e11.1.3.1 Face Recognition 169\u003c\/p\u003e \u003cp\u003e11.1.3.2 Hand Vein 170\u003c\/p\u003e \u003cp\u003e11.1.3.3 Signature Verification 170\u003c\/p\u003e \u003cp\u003e11.1.3.4 Iris Recognition 170\u003c\/p\u003e \u003cp\u003e11.1.3.5 Voice Recognition 170\u003c\/p\u003e \u003cp\u003e11.1.3.6 Fingerprints 171\u003c\/p\u003e \u003cp\u003e11.2 Literature Survey 172\u003c\/p\u003e \u003cp\u003e11.3 Biometric Identification in Cloud Computing 174\u003c\/p\u003e \u003cp\u003e11.3.1 How Biometric Authentication is Being Used on the Cloud Platform 176\u003c\/p\u003e \u003cp\u003e11.4 Models and Design Goals 177\u003c\/p\u003e \u003cp\u003e11.4.1 Models 177\u003c\/p\u003e \u003cp\u003e11.4.1.1 System Model 177\u003c\/p\u003e \u003cp\u003e11.4.1.2 Threat Model 177\u003c\/p\u003e \u003cp\u003e11.4.2 Design Goals 178\u003c\/p\u003e \u003cp\u003e11.5 Face Recognition Method as a Biometric Authentication 179\u003c\/p\u003e \u003cp\u003e11.6 Deep Learning Techniques for Big Data in Biometrics 180\u003c\/p\u003e \u003cp\u003e11.6.1 Issues and Challenges 181\u003c\/p\u003e \u003cp\u003e11.6.2 Deep Learning Strategies For Biometric Identification 182\u003c\/p\u003e \u003cp\u003e11.7 Conclusion 185\u003c\/p\u003e \u003cp\u003eReferences 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Application of Deep Learning in Cloud Security 189\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJaya Jain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 190\u003c\/p\u003e \u003cp\u003e12.2 Literature Review 191\u003c\/p\u003e \u003cp\u003e12.3 Deep Learning 192\u003c\/p\u003e \u003cp\u003e12.4 The Uses of Fields in Deep Learning 195\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 202\u003c\/p\u003e \u003cp\u003eReferences 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Real Time Cloud Based Intrusion Detection 207\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEkta Bafna\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 207\u003c\/p\u003e \u003cp\u003e13.2 Literature Review 209\u003c\/p\u003e \u003cp\u003e13.3 Incursion In Cloud 211\u003c\/p\u003e \u003cp\u003e13.3.1 Denial of Service (DoS) Attack 212\u003c\/p\u003e \u003cp\u003e13.3.2 Insider Attack 212\u003c\/p\u003e \u003cp\u003e13.3.3 User To Root (U2R) Attack 213\u003c\/p\u003e \u003cp\u003e13.3.4 Port Scanning 213\u003c\/p\u003e \u003cp\u003e13.4 Intrusion Detection System 213\u003c\/p\u003e \u003cp\u003e13.4.1 Signature-Based Intrusion Detection System (SIDS) 213\u003c\/p\u003e \u003cp\u003e13.4.2 Anomaly-Based Intrusion Detection System (AIDS) 214\u003c\/p\u003e \u003cp\u003e13.4.3 Intrusion Detection System Using Deep Learning 215\u003c\/p\u003e \u003cp\u003e13.5 Types of IDS in Cloud 216\u003c\/p\u003e \u003cp\u003e13.5.1 Host Intrusion Detection System 216\u003c\/p\u003e \u003cp\u003e13.5.2 Network Based Intrusion Detection System 217\u003c\/p\u003e \u003cp\u003e13.5.3 Distributed Based Intrusion Detection System 217\u003c\/p\u003e \u003cp\u003e13.6 Model of Deep Learning 218\u003c\/p\u003e \u003cp\u003e13.6.1 ConvNet Model 218\u003c\/p\u003e \u003cp\u003e13.6.2 Recurrent Neural Network 219\u003c\/p\u003e \u003cp\u003e13.6.3 Multi-Layer Perception Model 219\u003c\/p\u003e \u003cp\u003e13.7 KDD Dataset 221\u003c\/p\u003e \u003cp\u003e13.8 Evaluation 221\u003c\/p\u003e \u003cp\u003e13.9 Conclusion 223\u003c\/p\u003e \u003cp\u003eReferences 223\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Applications of Deep Learning in Cloud Security 225\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDisha Shrmali and Shweta Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 226\u003c\/p\u003e \u003cp\u003e14.1.1 Data Breaches 226\u003c\/p\u003e \u003cp\u003e14.1.2 Accounts Hijacking 227\u003c\/p\u003e \u003cp\u003e14.1.3 Insider Threat 227\u003c\/p\u003e \u003cp\u003e14.1.3.1 Malware Injection 227\u003c\/p\u003e \u003cp\u003e14.1.3.2 Abuse of Cloud Services 228\u003c\/p\u003e \u003cp\u003e14.1.3.3 Insecure APIs 228\u003c\/p\u003e \u003cp\u003e14.1.3.4 Denial of Service Attacks 228\u003c\/p\u003e \u003cp\u003e14.1.3.5 Insufficient Due Diligence 229\u003c\/p\u003e \u003cp\u003e14.1.3.6 Shared Vulnerabilities 229\u003c\/p\u003e \u003cp\u003e14.1.3.7 Data Loss 229\u003c\/p\u003e \u003cp\u003e14.2 Deep Learning Methods for Cloud Cyber Security 230\u003c\/p\u003e \u003cp\u003e14.2.1 Deep Belief Networks 230\u003c\/p\u003e \u003cp\u003e14.2.1.1 Deep Autoencoders 230\u003c\/p\u003e \u003cp\u003e14.2.1.2 Restricted Boltzmann Machines 232\u003c\/p\u003e \u003cp\u003e14.2.1.3 DBNs, RBMs, or Deep Autoencoders Coupled with Classification Layers 233\u003c\/p\u003e \u003cp\u003e14.2.1.4 Recurrent Neural Networks 233\u003c\/p\u003e \u003cp\u003e14.2.1.5 Convolutional Neural Networks 234\u003c\/p\u003e \u003cp\u003e14.2.1.6 Generative Adversarial Networks 235\u003c\/p\u003e \u003cp\u003e14.2.1.7 Recursive Neural Networks 236\u003c\/p\u003e \u003cp\u003e14.2.2 Applications of Deep Learning in Cyber Security 237\u003c\/p\u003e \u003cp\u003e14.2.2.1 Intrusion Detection and Prevention Systems (IDS\/IPS) 237\u003c\/p\u003e \u003cp\u003e14.2.2.2 Dealing with Malware 237\u003c\/p\u003e \u003cp\u003e14.2.2.3 Spam and Social Engineering Detection 238\u003c\/p\u003e \u003cp\u003e14.2.2.4 Network Traffic Analysis 238\u003c\/p\u003e \u003cp\u003e14.2.2.5 User Behaviour Analytics 238\u003c\/p\u003e \u003cp\u003e14.2.2.6 Insider Threat Detection 239\u003c\/p\u003e \u003cp\u003e14.2.2.7 Border Gateway Protocol Anomaly Detection 239\u003c\/p\u003e \u003cp\u003e14.2.2.8 Verification if Keystrokes were Typed by a Human 240\u003c\/p\u003e \u003cp\u003e14.3 Framework to Improve Security in Cloud Computing 240\u003c\/p\u003e \u003cp\u003e14.3.1 Introduction to Firewalls 241\u003c\/p\u003e \u003cp\u003e14.3.2 Importance of Firewalls 242\u003c\/p\u003e \u003cp\u003e14.3.2.1 Prevents the Passage of Unwanted Content 242\u003c\/p\u003e \u003cp\u003e14.3.2.2 Prevents Unauthorized Remote Access 243\u003c\/p\u003e \u003cp\u003e14.3.2.3 Restrict Indecent Content 243\u003c\/p\u003e \u003cp\u003e14.3.2.4 Guarantees Security Based on Protocol and IP Address 244\u003c\/p\u003e \u003cp\u003e14.3.2.5 Protects Seamless Operations in Enterprises 244\u003c\/p\u003e \u003cp\u003e14.3.2.6 Protects Conversations and Coordination Contents 244\u003c\/p\u003e \u003cp\u003e14.3.2.7 Restricts Online Videos and Games from Displaying Destructive Content 245\u003c\/p\u003e \u003cp\u003e14.3.3 Types of Firewalls 245\u003c\/p\u003e \u003cp\u003e14.3.3.1 Proxy-Based Firewalls 245\u003c\/p\u003e \u003cp\u003e14.3.3.2 Stateful Firewalls 246\u003c\/p\u003e \u003cp\u003e14.3.3.3 Next-Generation Firewalls (NGF) 247\u003c\/p\u003e \u003cp\u003e14.3.3.4 Web Application Firewalls (WAF) 247\u003c\/p\u003e \u003cp\u003e14.3.3.5 Working of WAF 248\u003c\/p\u003e \u003cp\u003e14.3.3.6 How Web Application Firewalls (WAF) Work 248\u003c\/p\u003e \u003cp\u003e14.3.3.7 Attacks that Web Application Firewalls Prevent 250\u003c\/p\u003e \u003cp\u003e14.3.3.8 Cloud WAF 251\u003c\/p\u003e \u003cp\u003e14.4 WAF Deployment 251\u003c\/p\u003e \u003cp\u003e14.4.1 Web Application Firewall (WAF) Security Models 252\u003c\/p\u003e \u003cp\u003e14.4.2 Firewall-as-a-Service (FWaaS) 252\u003c\/p\u003e \u003cp\u003e14.4.3 Basic Difference Between a Cloud Firewall and a Next-Generation Firewall (NGFW) 253\u003c\/p\u003e \u003cp\u003e14.4.4 Introduction and Effects of Firewall Network Parameters on Cloud Computing 253\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 254\u003c\/p\u003e \u003cp\u003eReferences 254\u003c\/p\u003e \u003cp\u003eAbout the Editors 257\u003c\/p\u003e \u003cp\u003eIndex 263 \u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52430915698968,"sku":"9781119760528","price":136.78,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119760528.jpg?v=1784765405","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/deep-learning-approaches-to-cloud-security-hardback-9781119760528","provider":"Freshly Printed Books","version":"1.0","type":"link"}