MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these…mehr
MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY
This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions
The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively.
Audience
Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
Produktdetails
Advances in Learning Analytics for Intelligent Cloud-IoT Systems
Rajdeep Chakraborty obtained his PhD in CSE from the University of Kalyani. He is currently an assistant professor in the Department of Computer Science and Engineering, Netaji Subhash Engineering College, Garia, Kolkata, India. He has several publications in reputed international journals and conferences and has authored a book on hardware cryptography. His field of interest is mainly in cryptography and computer security. Anupam Ghosh obtained his PhD in Engineering from Jadavpur University. He is currently a professor in the Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata. He has published more than 80 papers in reputed international journals and conferences. His field of interest is mainly in AI, machine learning, deep learning, image processing, soft computing, bioinformatics, IoT, data mining. Jyotsna Kumar Mandal obtained his PhD in CSE from Jadavpur University He has more than 450 publications in reputed international journals and conferences. His field of interest is mainly in coding theory, data and network security, remote sensing & GIS-based applications, data compression error corrections, information security, watermarking, steganography and document authentication, image processing, visual cryptography, MANET, wireless and mobile computing/security, unify computing, chaos theory, and applications.
Inhaltsangabe
Preface xix
Part I: Conceptual Aspects on Cloud and Applications of Machine Learning 1
1 Hybrid Cloud: A New Paradigm in Cloud Computing 3 Moumita Deb and Abantika Choudhury
1.1 Introduction 3
1.2 Hybrid Cloud 5
1.2.1 Architecture 6
1.2.2 Why Hybrid Cloud is Required? 6
1.2.3 Business and Hybrid Cloud 7
1.2.4 Things to Remember When Deploying Hybrid Cloud 8
1.3 Comparison Among Different Hybrid Cloud Providers 9
1.3.1 Cloud Storage and Backup Benefits 11
1.3.2 Pros and Cons of Different Service Providers 11
1.3.2.1 AWS Outpost 12
1.3.2.2 Microsoft Azure Stack 12
1.3.2.3 Google Cloud Anthos 12
1.3.3 Review on Storage of the Providers 13
1.3.3.1 AWS Outpost Storage 13
1.3.3.2 Google Cloud Anthos Storage 13
1.3.4 Pricing 15
1.4 Hybrid Cloud in Education 15
1.5 Significance of Hybrid Cloud Post-Pandemic 15
1.6 Security in Hybrid Cloud 16
1.6.1 Role of Human Error in Cloud Security 18
1.6.2 Handling Security Challenges 18
1.7 Use of AI in Hybrid Cloud 19
1.8 Future Research Direction 21
1.9 Conclusion 22
References 22
2 Recognition of Differentially Expressed Glycan Structure of H1N1 Virus Using Unsupervised Learning Framework 25 Shillpi Mishrra
2.1 Introduction 25
2.2 Proposed Methodology 27
2.3 Result 28
2.3.1 Description of Datasets 29
2.3.2 Analysis of Result 29
2.3.3 Validation of Results 31
2.3.3.1 T-Test (Statistical Validation) 31
2.3.3.2 Statistical Validation 33
2.3.4 Glycan Cloud 37
2.4 Conclusions and Future Work 38
References 39
3 Selection of Certain Cancer Mediating Genes Using a Hybrid Model Logistic Regression Supported by Principal Component Analysis (PC-LR) 41 Subir Hazra, Alia Nikhat Khurshid and Akriti
3.1 Introduction 41
3.2 Related Methods 44
3.3 Methodology 46
3.3.1 Description 47
3.3.2 Flowchart 49
3.3.3 Algorithm 49
3.3.4 Interpretation of the Algorithm 50
3.3.5 Illustration 50
3.4 Result 51
3.4.1 Description of the Dataset 51
3.4.2 Result Analysis 51
3.4.3 Result Set Validation 52
3.5 Application in Cloud Domain 56
3.6 Conclusion 58
References 59
Part II: Cloud Security Systems Using Machine Learning Techniques 61
4 Cost-Effective Voice-Controlled Real-Time Smart Informative Interface Design With Google Assistance Technology 63 Soumen Santra, Partha Mukherjee and Arpan Deyasi
4.1 Introduction 64
4.2 Home Automation System 65
4.2.1 Sensors 65
4.2.2 Protocols 66
4.2.3 Technologies 66
4.2.4 Advantages 67
4.2.5 Disadvantages 67
4.3 Literature Review 67
4.4 Role of Sensors and Microcontrollers in Smart Home Design 68
4.5 Motivation of the Project 70
4.6 Smart Informative and Command Accepting Interface 70
4.7 Data Flow Diagram 71
4.8 Components of Informative Interface 72
4.9 Results 73
4.9.1 Circuit Design 73
4.9.2 LDR Data 76
4.9.3 API Data 76
4.10 Conclusion 78
4.11 Future Scope 78
References 78
5 Symmetric Key and Artificial Neural Network With Mealy Machine: A Neoteric Model of Cryptosystem for Cloud Security 81 Anirban Bhowmik, Sunil Karforma and Joydeep Dey
Part I: Conceptual Aspects on Cloud and Applications of Machine Learning 1
1 Hybrid Cloud: A New Paradigm in Cloud Computing 3 Moumita Deb and Abantika Choudhury
1.1 Introduction 3
1.2 Hybrid Cloud 5
1.2.1 Architecture 6
1.2.2 Why Hybrid Cloud is Required? 6
1.2.3 Business and Hybrid Cloud 7
1.2.4 Things to Remember When Deploying Hybrid Cloud 8
1.3 Comparison Among Different Hybrid Cloud Providers 9
1.3.1 Cloud Storage and Backup Benefits 11
1.3.2 Pros and Cons of Different Service Providers 11
1.3.2.1 AWS Outpost 12
1.3.2.2 Microsoft Azure Stack 12
1.3.2.3 Google Cloud Anthos 12
1.3.3 Review on Storage of the Providers 13
1.3.3.1 AWS Outpost Storage 13
1.3.3.2 Google Cloud Anthos Storage 13
1.3.4 Pricing 15
1.4 Hybrid Cloud in Education 15
1.5 Significance of Hybrid Cloud Post-Pandemic 15
1.6 Security in Hybrid Cloud 16
1.6.1 Role of Human Error in Cloud Security 18
1.6.2 Handling Security Challenges 18
1.7 Use of AI in Hybrid Cloud 19
1.8 Future Research Direction 21
1.9 Conclusion 22
References 22
2 Recognition of Differentially Expressed Glycan Structure of H1N1 Virus Using Unsupervised Learning Framework 25 Shillpi Mishrra
2.1 Introduction 25
2.2 Proposed Methodology 27
2.3 Result 28
2.3.1 Description of Datasets 29
2.3.2 Analysis of Result 29
2.3.3 Validation of Results 31
2.3.3.1 T-Test (Statistical Validation) 31
2.3.3.2 Statistical Validation 33
2.3.4 Glycan Cloud 37
2.4 Conclusions and Future Work 38
References 39
3 Selection of Certain Cancer Mediating Genes Using a Hybrid Model Logistic Regression Supported by Principal Component Analysis (PC-LR) 41 Subir Hazra, Alia Nikhat Khurshid and Akriti
3.1 Introduction 41
3.2 Related Methods 44
3.3 Methodology 46
3.3.1 Description 47
3.3.2 Flowchart 49
3.3.3 Algorithm 49
3.3.4 Interpretation of the Algorithm 50
3.3.5 Illustration 50
3.4 Result 51
3.4.1 Description of the Dataset 51
3.4.2 Result Analysis 51
3.4.3 Result Set Validation 52
3.5 Application in Cloud Domain 56
3.6 Conclusion 58
References 59
Part II: Cloud Security Systems Using Machine Learning Techniques 61
4 Cost-Effective Voice-Controlled Real-Time Smart Informative Interface Design With Google Assistance Technology 63 Soumen Santra, Partha Mukherjee and Arpan Deyasi
4.1 Introduction 64
4.2 Home Automation System 65
4.2.1 Sensors 65
4.2.2 Protocols 66
4.2.3 Technologies 66
4.2.4 Advantages 67
4.2.5 Disadvantages 67
4.3 Literature Review 67
4.4 Role of Sensors and Microcontrollers in Smart Home Design 68
4.5 Motivation of the Project 70
4.6 Smart Informative and Command Accepting Interface 70
4.7 Data Flow Diagram 71
4.8 Components of Informative Interface 72
4.9 Results 73
4.9.1 Circuit Design 73
4.9.2 LDR Data 76
4.9.3 API Data 76
4.10 Conclusion 78
4.11 Future Scope 78
References 78
5 Symmetric Key and Artificial Neural Network With Mealy Machine: A Neoteric Model of Cryptosystem for Cloud Security 81 Anirban Bhowmik, Sunil Karforma and Joydeep Dey
5.1 Introduction 81 &
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Internetauftritt der buecher.de internetstores GmbH
Geschäftsführung: Monica Sawhney | Roland Kölbl | Günter Hilger
Sitz der Gesellschaft: Batheyer Straße 115 - 117, 58099 Hagen
Postanschrift: Bürgermeister-Wegele-Str. 12, 86167 Augsburg
Amtsgericht Hagen HRB 13257
Steuernummer: 321/5800/1497
