Deep Learning Approaches to Cloud Security: Deep Learning Approaches for Cloud Security

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
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
		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
EULA