Оглавление
Группа авторов. Deep Learning Approaches to Cloud Security
Table of Contents
List of Illustrations
List of Tables
Guide
Pages
Deep Learning Approaches to Cloud Security
Foreword
Preface
1. Biometric Identification Using Deep Learning for Advance Cloud Security
1.1 Introduction
1.2 Techniques of Biometric Identification. 1.2.1 Fingerprint Identification
1.2.2 Iris Recognition
1.2.3 Facial Recognition
1.2.4 Voice Recognition
1.3 Approaches
1.3.1 Feature Selection
1.3.2 Feature Extraction
1.3.3 Face Marking
1.3.4 Nearest Neighbor Approach
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
2.2.1 Basic Structure of Cloud Computing
2.2.2 Concept of Multi-Tenancy
2.2.3 Concept of Multi-Tenancy with Cloud Computing
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
3.3.1 EEG Signal Pre-Processing
3.3.1.1 Discrete Fourier Transform (DFT)
3.3.1.2 Least Mean Square (LMS) Algorithm
3.3.1.3 Discrete Cosine Transform (DCT)
3.3.2 Feature Extraction Techniques
3.3.3 Classification Techniques
3.4 BCI Applications
3.4.1 Possible BCI Uses
3.4.2 Communication
3.4.3 Movement Control
3.4.4 Environment Control
3.4.5 Locomotion
3.5 Cloud-Based EEG Overview
3.5.1 Data Backup and Restoration
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
4.3.1 Block Diagram
4.3.2 DC Motor Mechanism
4.3.3 Battery Bank
4.3.4 System Management Using IoT
4.3.5 Structure of Proposed System
4.3.6 Spoiler Design
4.3.7 Working Principle of Proposed System
4.3.8 Design and Analysis
4.4 Applications of IoT in Renewable Energy Resources
4.4.1 Wind Turbine Reliability Using IoT
4.4.2 Siting of Wind Resource Using IoT
4.4.3 Application of Renewable Energy in Medical Industries
4.4.4 Data Analysis Using Deep Learning
4.5 Conclusion
References
5. Background Mosaicing Model for Wide Area Surveillance System
5.1 Introduction
5.2 Related Work
5.3 Methodology
5.3.1 Feature Extraction
5.3.2 Background Deep Learning Model Based on Mosaic
5.3.3 Foreground Segmentation
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
7.2.1 Data Acquisition
7.2.2 Image Preprocessing
7.2.3 Segmentation
7.2.4 Feature Extraction
7.2.5 Classification
7.3 Results and Discussions. 7.3.1 Preprocessing
7.3.2 Classification
7.3.3 Validation
7.4 Conclusion
References
8. Convolutional Networks
8.1 Introduction
8.2 Convolution Operation
8.3 CNN
8.4 Practical Applications
8.4.1 Audio Data
8.4.2 Image Data
8.4.3 Text Data
8.5 Challenges of Profound Models
8.6 Deep Learning In Object Detection
8.7 CNN Architectures
8.8 Challenges of Item Location
8.8.1 Scale Variation Problem
8.8.2 Occlusion Problem
8.8.3 Deformation Problem
References
9. Categorization of Cloud Computing & Deep Learning
9.1 Introduction to Cloud Computing. 9.1.1 Cloud Computing
9.1.2 Cloud Computing: History and Evolution
9.1.3 Working of Cloud
9.1.4 Characteristics of Cloud Computing
9.1.5 Different Types of Cloud Computing Service Models
9.1.5.1 Infrastructure as A Service (IAAS)
9.1.5.2 Platform as a Service (PAAS)
9.1.5.3 Software as a Service (SAAS)
9.1.6 Cloud Computing Advantages and Disadvantages
9.1.6.1 Advantages of Cloud Computing
9.1.6.2 Disadvantages of Cloud Computing
9.2 Introduction to Deep Learning
9.2.1 History and Revolution of Deep Learning
9.2.1.1 Development of Deep Learning Algorithms
9.2.1.2 The FORTRAN Code for Back Propagation
9.2.1.3 Deep Learning from the 2000s and Beyond
9.2.1.4 The Cat Experiment
9.2.2 Neural Networks. 9.2.2.1 Artificial Neural Networks
9.2.2.2 Deep Neural Networks
9.2.3 Applications of Deep Learning. 9.2.3.1 Automatic Speech Recognition
9.2.3.2 Electromyography (EMG) Recognition
9.2.3.3 Image Recognition
9.2.3.4 Visual Art Processing
9.2.3.5 Natural Language Processing
9.2.3.6 Drug Discovery and Toxicology
9.2.3.7 Customer Relationship Management
9.2.3.8 Recommendation Systems
9.2.3.9 Bioinformatics
9.2.3.10 Medical Image Analysis
9.2.3.11 Mobile Advertising
9.2.3.12 Image Restoration
9.2.3.13 Financial Fraud Detection
9.2.3.14 Military
9.3 Conclusion
References
10. Smart Load Balancing in Cloud Using Deep Learning
10.1 Introduction
10.2 Load Balancing
10.2.1 Static Algorithm
10.2.2 Dynamic (Run-Time) Algorithms
10.3 Load Adjusting in Distributing Computing
10.3.1 Working of Load Balancing
10.4 Cloud Load Balancing Criteria (Measures)
10.5 Load Balancing Proposed for Cloud Computing
10.5.1 Calculation of Load Balancing in the Whole System
10.6 Load Balancing in Next Generation Cloud Computing
10.7 Dispersed AI Load Adjusting Methodology in Distributed Computing Administrations
10.7.1 Quantum Isochronous Parallel
10.7.2 Phase Isochronous Parallel
10.7.3 Dynamic Isochronous Coordinate Strategy
10.8 Adaptive-Dynamic Synchronous Coordinate Strategy
10.8.1 Adaptive Quick Reassignment (AdaptQR)
10.8.2 A-DIC (Adaptive-Dynamic Synchronous Parallel)
10.9 Conclusion
References
11. Biometric Identification for Advanced Cloud Security
11.1 Introduction. 11.1.1 Biometric Identification
11.1.2 Biometric Characteristic
11.1.3 Types of Biometric Data. 11.1.3.1 Face Recognition
11.1.3.2 Hand Vein
11.1.3.3 Signature Verification
11.1.3.4 Iris Recognition
11.1.3.5 Voice Recognition
11.1.3.6 Fingerprints
11.2 Literature Survey
11.3 Biometric Identification in Cloud Computing
11.3.1 How Biometric Authentication is Being Used on the Cloud Platform
11.4 Models and Design Goals. 11.4.1 Models. 11.4.1.1 System Model
11.4.1.2 Threat Model
11.4.2 Design Goals
11.5 Face Recognition Method as a Biometric Authentication
11.6 Deep Learning Techniques for Big Data in Biometrics
11.6.1 Issues and Challenges
11.6.2 Deep Learning Strategies For Biometric Identification
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
13.3.1 Denial of Service (DoS) Attack
13.3.2 Insider Attack
13.3.3 User To Root (U2R) Attack
13.3.4 Port Scanning
13.4 Intrusion Detection System
13.4.1 Signature-Based Intrusion Detection System (SIDS)
13.4.2 Anomaly-Based Intrusion Detection System (AIDS)
13.4.3 Intrusion Detection System Using Deep Learning
13.5 Types of IDS in Cloud. 13.5.1 Host Intrusion Detection System
13.5.2 Network Based Intrusion Detection System
13.5.3 Distributed Based Intrusion Detection System
13.6 Model of Deep Learning
13.6.1 ConvNet Model
13.6.2 Recurrent Neural Network
13.6.3 Multi-Layer Perception Model
13.7 KDD Dataset
13.8 Evaluation
13.9 Conclusion
References
14. Applications of Deep Learning in Cloud Security
14.1 Introduction
14.1.1 Data Breaches
14.1.2 Accounts Hijacking
14.1.3 Insider Threat
14.1.3.1 Malware Injection
14.1.3.2 Abuse of Cloud Services
14.1.3.3 Insecure APIs
14.1.3.4 Denial of Service Attacks
14.1.3.5 Insufficient Due Diligence
14.1.3.6 Shared Vulnerabilities
14.1.3.7 Data Loss
14.2 Deep Learning Methods for Cloud Cyber Security
14.2.1 Deep Belief Networks
14.2.1.1 Deep Autoencoders
14.2.1.2 Restricted Boltzmann Machines
14.2.1.3 DBNs, RBMs, or Deep Autoencoders Coupled with Classification Layers
14.2.1.4 Recurrent Neural Networks
14.2.1.5 Convolutional Neural Networks
14.2.1.6 Generative Adversarial Networks
14.2.1.7 Recursive Neural Networks
14.2.2 Applications of Deep Learning in Cyber Security
14.2.2.1 Intrusion Detection and Prevention Systems (IDS/IPS)
14.2.2.2 Dealing with Malware
14.2.2.3 Spam and Social Engineering Detection
14.2.2.4 Network Traffic Analysis
14.2.2.5 User Behaviour Analytics
14.2.2.6 Insider Threat Detection
14.2.2.7 Border Gateway Protocol Anomaly Detection
14.2.2.8 Verification if Keystrokes were Typed by a Human
14.3 Framework to Improve Security in Cloud Computing
14.3.1 Introduction to Firewalls
14.3.2 Importance of Firewalls
14.3.2.1 Prevents the Passage of Unwanted Content
14.3.2.2 Prevents Unauthorized Remote Access
14.3.2.3 Restrict Indecent Content
14.3.2.4 Guarantees Security Based on Protocol and IP Address
14.3.2.5 Protects Seamless Operations in Enterprises
14.3.2.6 Protects Conversations and Coordination Contents
14.3.2.7 Restricts Online Videos and Games from Displaying Destructive Content
14.3.3 Types of Firewalls. 14.3.3.1 Proxy-Based Firewalls
14.3.3.2 Stateful Firewalls
14.3.3.3 Next-Generation Firewalls (NGF)
14.3.3.4 Web Application Firewalls (WAF)
14.3.3.5 Working of WAF
14.3.3.6 How Web Application Firewalls (WAF) Work
14.3.3.7 Attacks that Web Application Firewalls Prevent
14.3.3.8 Cloud WAF
14.4 WAF Deployment
14.4.1 Web Application Firewall (WAF) Security Models
14.4.2 Firewall-as-a-Service (FWaaS)
14.4.3 Basic Difference Between a Cloud Firewall and a Next-Generation Firewall (NGFW)
14.4.4 Introduction and Effects of Firewall Network Parameters on Cloud Computing
14.5 Conclusion
References
About the Editors
Index
Also of Interest
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