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Deep Learning Approaches to Cloud Security [Kõva köide]

Edited by (Aryabhatta Engineering College and Research Centre, India; Government University, MDS Ajmer), Edited by , Edited by (Manav Rachna Internationa), Edited by (University of Madras; Aryabhatta Engineering College in Ajmer; Maharshi Dayanand Saraswati University in Ajmer), Edited by
  • Formaat: Hardback, 304 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Ilmumisaeg: 25-Jan-2022
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119760526
  • ISBN-13: 9781119760528
Teised raamatud teemal:
Deep Learning Approaches to Cloud SecuritySuurem pilt
  • Formaat: Hardback, 304 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Ilmumisaeg: 25-Jan-2022
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119760526
  • ISBN-13: 9781119760528
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119760528.html
Märksõnad:
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

Foreword xv
Preface xvii
1 Biometric Identification Using Deep Learning for Advance Cloud Security
1(14)
Navani Siroya
Manju Mandot
1.1 Introduction
2(1)
1.2 Techniques of Biometric Identification
3(3)
1.2.1 Fingerprint Identification
3(1)
1.2.2 Iris Recognition
4(1)
1.2.3 Facial Recognition
4(1)
1.2.4 Voice Recognition
5(1)
1.3 Approaches
6(3)
1.3.1 Feature Selection
6(1)
1.3.2 Feature Extraction
6(1)
1.3.3 Face Marking
7(1)
1.3.4 Nearest Neighbor Approach
8(1)
1.4 Related Work, A Review
9(1)
1.5 Proposed Work
10(2)
1.6 Future Scope
12(1)
1.7 Conclusion
12(3)
References
12(3)
2 Privacy in Multi-Tenancy Cloud Using Deep Learning
15(12)
Shweta Solanki
Prafull Narooka
2.1 Introduction
15(1)
2.2 Basic Structure
16(5)
2.2.1 Basic Structure of Cloud Computing
17(1)
2.2.2 Concept of Multi-Tenancy
18(1)
2.2.3 Concept of Multi-Tenancy with Cloud Computing
19(2)
2.3 Privacy in Cloud Environment Using Deep Learning
21(1)
2.4 Privacy in Multi-Tenancy with Deep Learning Concept
22(1)
2.5 Related Work
23(1)
2.6 Conclusion
24(3)
References
25(2)
3 Emotional Classification Using EEG Signals and Facial Expression: A Survey
27(16)
S. J. Savitha
Dr. M. Paulraj
K. Saranya
3.1 Introduction
27(2)
3.2 Related Works
29(3)
3.3 Methods
32(2)
3.3.1 EEG Signal Pre-Processing
32(1)
3.3.1.1 Discrete Fourier Transform (DFT)
32(1)
3.3.1.2 Least Mean Square (LMS) Algorithm
32(1)
3.3.1.3 Discrete Cosine Transform (DCT)
33(1)
3.3.2 Feature Extraction Techniques
33(1)
3.3.3 Classification Techniques
33(1)
3.4 BCI Applications
34(4)
3.4.1 Possible BCI Uses
36(1)
3.4.2 Communication
36(1)
3.4.3 Movement Control
36(1)
3.4.4 Environment Control
37(1)
3.4.5 Locomotion
38(1)
3.5 Cloud-Based EEG Overview
38(2)
3.5.1 Data Backup and Restoration
39(1)
3.6 Conclusion
40(3)
References
40(3)
4 Effective and Efficient Wind Power Generation Using Bifarious Solar PV System
43(20)
R. Amirtha Katesa Sai Raj
M. Aran Kumar
S. Dinesh
U. Harisudhan
Dr. R. Uthirasamy
4.1 Introduction
44(1)
4.2 Study of Bi-Facial Solar Panel
45(1)
4.3 Proposed System
46(7)
4.3.1 Block Diagram
46(1)
4.3.2 DC Motor Mechanism
47(1)
4.3.3 Battery Bank
48(1)
4.3.4 System Management Using IoT
48(2)
4.3.5 Structure of Proposed System
50(1)
4.3.6 Spoiler Design
51(1)
4.3.7 Working Principle of Proposed System
52(1)
4.3.8 Design and Analysis
53(1)
4.4 Applications of IoT in Renewable Energy Resources
53(6)
4.4.1 Wind Turbine Reliability Using IoT
54(1)
4.4.2 Siting of Wind Resource Using IoT
55(1)
4.4.3 Application of Renewable Energy in Medical Industries
56(1)
4.4.4 Data Analysis Using Deep Learning
57(2)
4.5 Conclusion
59(4)
References
59(4)
5 Background Mosaicing Model for Wide Area Surveillance System
63(12)
Dr. E. Komagal
5.1 Introduction
64(1)
5.2 Related Work
64(1)
5.3 Methodology
65(5)
5.3.1 Feature Extraction
66(1)
5.3.2 Background Deep Learning Model Based on Mosaic
67(3)
5.3.3 Foreground Segmentation
70(1)
5.4 Results and Discussion
70(2)
5.5 Conclusion
72(3)
References
72(3)
6 Prediction of CKD Stage 1 Using Three Different Classifiers
75(18)
K. Thamizharasan
P. Yamini
A. Shimola
S. Sudha
6.1 Introduction
75(3)
6.2 Materials and Methods
78(6)
6.3 Results and Discussion
84(5)
6.4 Conclusions and Future Scope
89(4)
References
89(4)
7 Classification of MRI Images to Aid in Diagnosis of Neurological Disorder Using SVM
93(16)
Phavithra Selvaraj
M.S. Sruthi
M. Sridaran
Dr. M.C. Jobin Christ
7.1 Introduction
93(2)
7.2 Methodology
95(5)
7.2.1 Data Acquisition
95(1)
7.2.2 Image Preprocessing
96(1)
7.2.3 Segmentation
97(1)
7.2.4 Feature Extraction
98(1)
7.2.5 Classification
99(1)
7.3 Results and Discussions
100(6)
7.3.1 Preprocessing
100(3)
7.3.2 Classification
103(1)
7.3.3 Validation
104(2)
7.4 Conclusion
106(3)
References
106(3)
8 Convolutional Networks
109(14)
Simran Kaur
Rashmi Agrawal
8.1 Introduction
110(1)
8.2 Convolution Operation
110(1)
8.3 CNN
110(2)
8.4 Practical Applications
112(1)
8.4.1 Audio Data
112(1)
8.4.2 Image Data
112(1)
8.4.3 Text Data
113(1)
8.5 Challenges of Profound Models
113(1)
8.6 Deep Learning In Object Detection
114(1)
8.7 CNN Architectures
114(4)
8.8 Challenges of Item Location
118(5)
8.8.1 Scale Variation Problem
118(1)
8.8.2 Occlusion Problem
119(1)
8.8.3 Deformation Problem
120(1)
References
121(2)
9 Categorization of Cloud Computing & Deep Learning
123(22)
Disha Shrmali
9.1 Introduction to Cloud Computing
123(10)
9.1.1 Cloud Computing
123(1)
9.1.2 Cloud Computing: History and Evolution
124(1)
9.1.3 Working of Cloud
125(2)
9.1.4 Characteristics of Cloud Computing
127(1)
9.1.5 Different Types of Cloud Computing Service Models
128(1)
9.1.5.1 Infrastructure as A Service (IAAS)
128(1)
9.1.5.2 Platform as a Service (PAAS)
129(1)
9.1.5.3 Software as a Service (SAAS)
129(1)
9.1.6 Cloud Computing Advantages and Disadvantages
130(1)
9.1.6.1 Advantages of Cloud Computing
130(2)
9.1.6.2 Disadvantages of Cloud Computing
132(1)
9.2 Introduction to Deep Learning
133(9)
9.2.1 History and Revolution of Deep Learning
134(1)
9.2.1.1 Development of Deep Learning Algorithms
134(1)
9.2.1.2 The FORTRAN Code for Back Propagation
135(1)
9.2.1.3 Deep Learning from the 2000s and Beyond
135(1)
9.2.1.4 The Cat Experiment
136(1)
9.2.2 Neural Networks
137(1)
9.2.2.1 Artificial Neural Networks
137(1)
9.2.2.2 Deep Neural Networks
138(1)
9.2.3 Applications of Deep Learning
138(1)
9.2.3.1 Automatic Speech Recognition
138(1)
9.2.3.2 Electromyography (EMG) Recognition
139(1)
9.2.3.3 Image Recognition
139(1)
9.2.3.4 Visual Art Processing
140(1)
9.2.3.5 Natural Language Processing
140(1)
9.2.3.6 Drug Discovery and Toxicology
140(1)
9.2.3.7 Customer Relationship Management
141(1)
9.2.3.8 Recommendation Systems
141(1)
9.2.3.9 Bioinformatics
141(1)
9.2.3.10 Medical Image Analysis
141(1)
9.2.3.11 Mobile Advertising
141(1)
9.2.3.12 Image Restoration
142(1)
9.2.3.13 Financial Fraud Detection
142(1)
9.2.3.14 Military
142(1)
9.3 Conclusion
142(3)
References
143(2)
10 Smart Load Balancing in Cloud Using Deep Learning
145(22)
Astha Parihar
Shweta Sharma
10.1 Introduction
146(1)
10.2 Load Balancing
147(2)
10.2.1 Static Algorithm
148(1)
10.2.2 Dynamic (Run-Time) Algorithms
148(1)
10.3 Load Adjusting in Distributing Computing
149(3)
10.3.1 Working of Load Balancing
151(1)
10.4 Cloud Load Balancing Criteria (Measures)
152(1)
10.5 Load Balancing Proposed for Cloud Computing
153(2)
10.5.1 Calculation of Load Balancing in the Whole System
154(1)
10.6 Load Balancing in Next Generation Cloud Computing
155(2)
10.7 Dispersed AI Load Adjusting Methodology in Distributed Computing Administrations
157(4)
10.7.1 Quantum Isochronous Parallel
158(1)
10.7.2 Phase Isochronous Parallel
159(2)
10.7.3 Dynamic Isochronous Coordinate Strategy
161(1)
10.8 Adaptive-Dynamic Synchronous Coordinate Strategy
161(3)
10.8.1 Adaptive Quick Reassignment (AdaptQR)
162(1)
10.8.2 A-DIC (Adaptive-Dynamic Synchronous Parallel)
163(1)
10.9 Conclusion
164(3)
References
165(2)
11 Biometric Identification for Advanced Cloud Security
167(22)
Yojna Khandelwal
Kapil Chauhan
11.1 Introduction
168(4)
11.1.1 Biometric Identification
168(1)
11.1.2 Biometric Characteristic
169(1)
11.1.3 Types of Biometric Data
169(1)
11.1.3.1 Face Recognition
169(1)
11.1.3.2 Hand Vein
170(1)
11.1.3.3 Signature Verification
170(1)
11.1.3.4 Iris Recognition
170(1)
11.1.3.5 Voice Recognition
170(1)
11.1.3.6 Fingerprints
171(1)
11.2 Literature Survey
172(2)
11.3 Biometric Identification in Cloud Computing
174(3)
11.3.1 How Biometric Authentication is Being Used on the Cloud Platform
176(1)
11.4 Models and Design Goals
177(2)
11.4.1 Models
177(1)
11.4.1.1 System Model
177(1)
11.4.1.2 Threat Model
177(1)
11.4.2 Design Goals
178(1)
11.5 Face Recognition Method as a Biometric Authentication
179(1)
11.6 Deep Learning Techniques for Big Data in Biometrics
180(5)
11.6.1 Issues and Challenges
181(1)
11.6.2 Deep Learning Strategies For Biometric Identification
182(3)
11.7 Conclusion
185(4)
References
185(4)
12 Application of Deep Learning in Cloud Security
189(18)
Jaya Jain
12.1 Introduction
190(1)
12.2 Literature Review
191(1)
12.3 Deep Learning
192(3)
12.4 The Uses of Fields in Deep Learning
195(7)
12.5 Conclusion
202(5)
References
203(4)
13 Real Time Cloud Based Intrusion Detection
207(18)
Ekta Bafna
13.1 Introduction
207(2)
13.2 Literature Review
209(2)
13.3 Incursion In Cloud
211(2)
13.3.1 Denial of Service (DoS) Attack
212(1)
13.3.2 Insider Attack
212(1)
13.3.3 User To Root (U2R) Attack
213(1)
13.3.4 Port Scanning
213(1)
13.4 Intrusion Detection System
213(3)
13.4.1 Signature-Based Intrusion Detection System (SIDS)
213(1)
13.4.2 Anomaly-Based Intrusion Detection System (AIDS)
214(1)
13.4.3 Intrusion Detection System Using Deep Learning
215(1)
13.5 Types of IDS in Cloud
216(2)
13.5.1 Host Intrusion Detection System
216(1)
13.5.2 Network Based Intrusion Detection System
217(1)
13.5.3 Distributed Based Intrusion Detection System
217(1)
13.6 Model of Deep Learning
218(3)
13.6.1 ConvNet Model
218(1)
13.6.2 Recurrent Neural Network
219(1)
13.6.3 Multi-Layer Perception Model
219(2)
13.7 KDDDataset
221(1)
13.8 Evaluation
221(2)
13.9 Conclusion
223(2)
References
223(2)
14 Applications of Deep Learning in Cloud Security
225(32)
Disha Shrmali
Shweta Sharma
14.1 Introduction
226(4)
14.1.1 Data Breaches
226(1)
14.1.2 Accounts Hijacking
227(1)
14.1.3 Insider Threat
227(1)
14.1.3.1 Malware Injection
227(1)
14.1.3.2 Abuse of Cloud Services
228(1)
14.1.3.3 Insecure APIs
228(1)
14.1.3.4 Denial of Service Attacks
228(1)
14.1.3.5 Insufficient Due Diligence
229(1)
14.1.3.6 Shared Vulnerabilities
229(1)
14.1.3.7 Data Loss
229(1)
14.2 Deep Learning Methods for Cloud Cyber Security
230(10)
14.2.1 Deep Belief Networks
230(1)
14.2.1.1 Deep Autoencoders
230(2)
14.2.1.2 Restricted Boltzmann Machines
232(1)
14.2.1.3 DBNs, RBMs, or Deep Autoencoders Coupled with Classification Layers
233(1)
14.2.1.4 Recurrent Neural Networks
233(1)
14.2.1.5 Convolutional Neural Networks
234(1)
14.2.1.6 Generative Adversarial Networks
235(1)
14.2.1.7 Recursive Neural Networks
236(1)
14.2.2 Applications of Deep Learning in Cyber Security
237(1)
14.2.2.1 Intrusion Detection and Prevention Systems (IDS/IPS)
237(1)
14.2.2.2 Dealing with Malware
237(1)
14.2.2.3 Spam and Social Engineering Detection
238(1)
14.2.2.4 Network Traffic Analysis
238(1)
14.2.2.5 User Behaviour Analytics
238(1)
14.2.2.6 Insider Threat Detection
239(1)
14.2.2.7 Border Gateway Protocol Anomaly Detection
239(1)
14.2.2.8 Verification if Keystrokes were Typed by a Human
240(1)
14.3 Framework to Improve Security in Cloud Computing
240(11)
14.3.1 Introduction to Firewalls
241(1)
14.3.2 Importance of Firewalls
242(1)
14.3.2.1 Prevents the Passage of Unwanted Content
242(1)
14.3.2.2 Prevents Unauthorized Remote Access
243(1)
14.3.2.3 Restrict Indecent Content
243(1)
14.3.2.4 Guarantees Security Based on Protocol and IP Address
244(1)
14.3.2.5 Protects Seamless Operations in Enterprises
244(1)
14.3.2.6 Protects Conversations and Coordination Contents
244(1)
14.3.2.7 Restricts Online Videos and Games from Displaying Destructive Content
245(1)
14.3.3 Types of Firewalls
245(1)
14.3.3.1 Proxy-Based Firewalls
245(1)
14.3.3.2 Stateful Firewalls
246(1)
14.3.3.3 Next-Generation Firewalls (NGF)
247(1)
14.3.3.4 Web Application Firewalls (WAF)
247(1)
14.3.3.5 Working of WAF
248(1)
14.3.3.6 How Web Application Firewalls (WAF) Work
248(2)
14.3.3.7 Attacks that Web Application Firewalls Prevent
250(1)
14.3.3.8 Cloud WAF
251(1)
14.4 WAF Deployment
251(3)
14.4.1 Web Application Firewall (WAF) Security Models
252(1)
14.4.2 Firewall-as-a-Service (FWaaS)
252(1)
14.4.3 Basic Difference Between a Cloud Firewall and a Next-Generation Firewall (NGFW)
253(1)
14.4.4 Introduction and Effects of Firewall Network Parameters on Cloud Computing
253(1)
14.5 Conclusion
254(3)
References
254(3)
About the Editors 257(6)
Index 263
Pramod Singh Rathore, PhD, is an assistant professor in the computer science and engineering department at the Aryabhatta Engineering College and Research Centre, Ajmer, Rajasthan, India and is also visiting faculty at the Government University, MDS Ajmer. He has over eight years of teaching experience and more than 45 publications in peer-reviewed journals, books, and conferences. He has also co-authored and edited numerous books with a variety of global publishers, such as the imprint, Wiley-Scrivener.

Vishal Dutt, PhD, received his doctorate in computer science from the University of Madras, and he is an assistant professor in the computer science and engineering department at the Aryabhatta Engineering College in Ajmer, as well as visiting faculty at Maharshi Dayanand Saraswati University in Ajmer. He has four years of teaching experience and has more than 22 publications in peer-reviewed scientific and technical journals. He has also been working as a freelance writer for more than six years in the fields of data analytics, Java, Assembly Programmer, Desktop Designer, and Android Developer.

Rashmi Agrawal, PhD, is a professor in the Department of Computer Applications at Manav Rachna International Institute of Research and Studies in Faridabad, India. She has over 18 years of experience in teaching and research and is a book series editor for a series on big data and machine learning. She has authored or coauthored numerous research papers in peer-reviewed scientific and technical journals and conferences and has also edited or authored books with a number of large book publishers, in imprints such as Wiley-Scrivener. She is also an active reviewer and editorial board member in various journals.

Satya Murthy Sasubilli is a solutions architect with the Huntington National Bank, having received his masters in computer applications from the University of Madras, India. He has more than 15 years of experience in cloud-based technologies like big data solutions, cloud infrastructure, digital analytics delivery, data warehousing, and many others. He has worked with many Fortune 500 organizations, such as Infosys, Capgemini, and others and is an active reviewer for several scientific and technical journals.

Srinivasa Rao Swarna is a program manager and senior data architect at Tata Consultancy Services in the USA. He received his BTech in chemical engineering from Jawaharlal Nehru Technological University, Hyderabad, India and completed his internship at Volkswagen AG, Wolfsburg, Germany in 2004. He has over 16 years of experience in this area, having worked with many Fortune 500 companies, and he is a frequent reviewer for several scientific and technical journals.