Deep Learning Approaches to Cloud Security. Группа авторов

10 Smart Load Balancing in Cloud Using Deep Learning 10.1 Introduction 10.2 Load Balancing 10.3 Load Adjusting in Distributing Computing 10.4 Cloud Load Balancing Criteria (Measures) 10.5 Load Balancing Proposed for Cloud Computing 10.6 Load Balancing in Next Generation Cloud Computing 10.7 Dispersed AI Load Adjusting Methodology in Distributed Computing Administrations 10.8 Adaptive-Dynamic Synchronous Coordinate Strategy 10.9 Conclusion References

      16  11 Biometric Identification for Advanced Cloud Security 11.1 Introduction 11.2 Literature Survey 11.3 Biometric Identification in Cloud Computing 11.4 Models and Design Goals 11.5 Face Recognition Method as a Biometric Authentication 11.6 Deep Learning Techniques for Big Data in Biometrics 11.7 Conclusion References

      17  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

      18  13 Real Time Cloud Based Intrusion Detection 13.1 Introduction 13.2 Literature Review 13.3 Incursion In Cloud 13.4 Intrusion Detection System 13.5 Types of IDS in Cloud 13.6 Model of Deep Learning 13.7 KDD Dataset 13.8 Evaluation 13.9 Conclusion References

      19  14 Applications of Deep Learning in Cloud Security 14.1 Introduction 14.2 Deep Learning Methods for Cloud Cyber Security 14.3 Framework to Improve Security in Cloud Computing 14.4 WAF Deployment 14.5 Conclusion References

      20  About the Editors

      21  Index

      22  End User License Agreement

      List of Illustrations

      1 Chapter 1Figure 1.1 Biometric modalities [2].Figure 1.2 Applications of Face-Land marking [4].Figure 1.3 System architecture.

      2 Chapter 2Figure 2.1 Cloud computing services [3].Figure 2.2 Types of tenant [4].Figure 2.3 Multi-Tenancy models [5].Figure 2.4 Multi-Tenant Cloud structure [6].Figure 2.5 Concept of Cloud Security [7].Figure 2.6 Securities in Multi-Cloud environments [7].Figure 2.7 Multi-Tenancy services [8].

      3 Chapter 4Figure 4.1 Survey of WECS.Figure 4.2 Bi-facial solar panel.Figure 4.3 Block diagram of the proposed system.Figure 4.4 DC motor with gear arrangement.Figure 4.5 Structure of proposed system.Figure 4.6 (a) Diagram of spoiler structure, (b) structure of spoiler design.Figure 4.7 Snapshot of bifarious solar panel in spoiler structure.Figure 4.8 Experimental setup of the proposed system.Figure 4.9 IoT features.Figure 4.10 Wind speed using IoT.Figure 4.11 Monthly average wind speed using IoT.Figure 4.12 Wind rose.Figure 4.13 Turbulence intensity using IoT.Figure 4.14 Turbulence intensity vs. wind speed (m/s).Figure 4.15 Comparison of gradient boosting and XG boost.

      4 Chapter 5Figure 5.1 Proposed methodology.Figure 5.2 MICC dataset background mosaic model.Figure 5.3 Comparison of mosaic features.Figure 5.4 (a) Input frame 220 on VIP dataset, (b) Extracted SURF features, (c) ...Figure 5.5 (a) Input frame 220 on MICC dataset, (b) Extracted SURF features, (c)...

      5 Chapter 6Figure 6.1 Proposed workflow.Figure 6.2 Working of decision tree.Figure 6.3 Main Window displaying user interface.Figure 6.4 Mean & standard deviation for urea and glucose (training data).Figure 6.5 Mean & standard deviation for urea and glucose (test data).Figure 6.6 Mean & standard deviation for creatinine (training & test data).Figure 6.7 Comparative analysis of accuracy & error of different classifiers cla...

      6 Chapter 7Figure 7.1 FlowchartFigure 7.2 Filtration and CLAHE performed in the image.Figure 7.3 Skull Stripping.Figure 7.4 Otsu’s segmentation.Figure 7.5 Multi-class SVM training results.Figure 7.6 Multi-class SVM testing results.

      7 Chapter