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Predictive analytics in cloud, fog, and edge computing : perspectives and practices of Blockchain, IoT, and 5G / / edited by Hiren Kumar Thakkar [and three others]
| Predictive analytics in cloud, fog, and edge computing : perspectives and practices of Blockchain, IoT, and 5G / / edited by Hiren Kumar Thakkar [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (252 pages) |
| Disciplina | 621.38456 |
| Soggetto topico |
Blockchains (Databases)
5G mobile communication systems Cloud computing |
| ISBN | 3-031-18034-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgement -- Contents -- Collaboration of IoT and Cloud Computing Towards HealthcareSecurity -- 1 Introduction -- 2 Inspiration -- 3 Related Work and Background -- 4 Cloud Computing Deployment Models -- 4.1 Public Internet -- 4.2 Corporate Cloud -- 4.3 Cloud Hybrid -- 4.4 Cloud Provider -- 5 Utility Computing Service Models -- 5.1 Software as a Service (SaaS) -- 5.2 Infrastructure as a Service (IaaS) -- 5.3 Platform as a Service (PaaS) -- 6 Security Issues -- 7 Threats in Cloud Computing -- 7.1 Compromised Identities and Broken Security -- 7.2 Data Infringement -- 7.3 Hacked Frontier and APIs -- 7.4 Manipulated System Vulnerabilities -- 7.5 Permanent Data Loss -- 7.6 Inadequate Assiduity -- 7.7 Cloud Service Inattention -- 7.8 DoS Attacks -- 7.9 Security Challenges in Cloud Infrastructure -- 7.9.1 Security Challenges -- 7.9.2 Challenges of Deployed Models -- 7.9.3 Resource Pooling -- 7.9.4 Unencrypted Data -- 7.9.5 Identity Management and Authentication -- 7.9.6 Network Issues -- 7.10 Point at Issue in the IoT Health Care Framework -- 7.10.1 Reliability -- 7.10.2 Discretion -- 7.10.3 Solitude -- 7.10.4 Unintended Efforts -- 7.11 Challenges -- 7.11.1 Security -- 7.11.2 Confidentiality -- 7.11.3 Assimilation -- 7.11.4 Business Illustration -- 7.12 Dispensing Refined Patient Supervision -- 7.13 Character of IoT in Healthcare -- 7.14 Conclusion -- 7.15 Future Work -- References -- Robust, Reversible Medical Image Watermarking for Transmission of Medical Images over Cloud in Smart IoT Healthcare -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 3.1 EHR Insertion (Embedding) and Retrieval (Extraction) -- 3.2 EHR Encryption and Decryption -- 4 Experimental Results and Discussion -- 5 Conclusions -- References -- The Role of Blockchain in Cloud Computing -- 1 Blockchain -- 1.1 Introduction -- 1.2 Characteristics.
1.2.1 Immutability -- 1.2.2 Distributed -- 1.2.3 Enhanced Security -- 1.2.4 Distributed Ledgers -- 1.2.5 Faster Settlement -- 1.2.6 Working of Blockchain -- 1.3 Major Implementations -- 1.3.1 Cryptocurrencies -- 1.3.2 Smart Contracts -- 1.3.3 Monetary Services -- 1.3.4 Games -- 1.4 Blockchain Types -- 1.5 There Are Mainly 4 Types of Blockchain as Shown in Table 1 -- 1.5.1 Public Blockchain Networks -- 1.5.2 Exclusive Blockchain Networks -- 1.5.3 Hybrid Blockchain Networks -- 1.5.4 Consortium Networks -- 1.6 Advantages -- 1.6.1 Secure -- 1.6.2 There Will Be No Intervention from Third Parties -- 1.6.3 Safe Transactions -- 1.6.4 Automation -- 1.7 Disadvantages -- 1.7.1 High Implementation Cost -- 1.7.2 Incompetency -- 1.7.3 Private Keys -- 1.7.4 Storage Capacity -- 2 Cloud Computing -- 2.1 What Is Cloud Computing? -- 2.2 Deployment Models in Cloud -- 2.2.1 Public Cloud -- 2.2.2 Private Cloud -- 2.2.3 Hybrid Cloud -- 2.2.4 Community Cloud -- 2.3 Implementations of Cloud Computing -- 2.3.1 Web Based Services -- 2.3.2 Software as a Service -- 2.3.3 Infrastructure as a Service -- 2.3.4 Platform as a Service -- 2.4 Comparison of Cloud Computing Model with Traditional Model -- 2.4.1 Persistency -- 2.4.2 Automation -- 2.4.3 Cost -- 2.4.4 Security -- 2.5 Advantages of Cloud Computing -- 2.5.1 Cost Efficiency -- 2.5.2 Backup and Recovery -- 2.5.3 Integration of Software -- 2.5.4 Information Availability -- 2.5.5 Deployment -- 2.5.6 Easier Scale for Services and Delivery of New Services -- 2.6 Challenges of Cloud Computing -- 2.6.1 Technical Problems -- 2.6.2 Certainty -- 2.6.3 Vulnerable Attacks -- 2.6.4 Suspension -- 2.6.5 Inflexibility -- 2.6.6 Lack of Assistance -- 2.7 Integration of Cloud Computing with Block Chain -- 2.7.1 The Advantages of Combining Cloud and Blockchain Technology -- 2.7.2 Blockchain Support for Cloud Computing. 2.7.3 Deduplication of Data in the Cloud with Blockchain -- 2.7.4 Access Control Based on Blockchain in Cloud -- References -- Analysis and Prediction of Plant Growth in a Cloud-Based Smart Sensor Controlled Environment -- 1 Introduction -- 2 Literature Survey -- 3 IoT in Greenhouse -- 3.1 Architecture -- 3.2 Cloud Implementation -- 3.3 Hardware Components (Fig. 2) -- 4 System Overview -- 4.1 Dataset -- 4.2 Data Preprocessing -- 4.3 LightGBM -- 4.4 Training and Building the Model -- 5 Results and Explanation -- 6 Conclusion -- References -- Cloud-Based IoT Controlled System Model for Plant DiseaseMonitoring -- 1 Introduction -- 2 Literature Survey -- 3 IoT Controlled Device -- 4 Cloud Architecture -- 5 Methodology -- 5.1 HOG Filter -- 6 Experimental Analysis -- 6.1 Analysis Using Artificial Neural Network -- 6.2 Analysis Using Convolutional Neural Network -- 7 Conclusion -- References -- Design and Usage of a Digital E-Pharmacy Application Framework -- 1 Introduction -- 2 Literature Survey -- 3 Utilization of Cloud in Health Care -- 4 Redefining E-Pharmacy Domain -- 5 Impact of Cloud Computing in Pharmacy -- 6 Model Design and Implementation -- 7 Basic Structure of the Cloud Based E-Pharmacy Application -- 8 Security Provided by the Application -- 8.1 XSS Security (Cross Site Scripting) -- 8.2 CSRF Token (Cross Site Request Forgery) -- 8.3 SQL Injection Security -- 8.4 User Upload Security -- 9 Results and Discussion -- 10 Important Features of the Application -- 11 Critical Goals of the Application -- 12 Benefits of the Model -- 13 Summary/Conclusion -- References -- Serverless Data Pipelines for IoT Data Analytics: A Cloud Vendors Perspective and Solutions -- 1 Introduction -- 1.1 Motivation -- 1.2 Contributions -- 2 Background -- 2.1 Internet of Things -- 2.2 Serverless Data Pipelines for IoT Data Processing -- 3 Literature Survey. 4 Cloud Service Providers (CSP) and IoT Solutions -- 4.1 Edge Tier -- 4.1.1 Comparison of AWS IoT Greengrass and Azure IoT Edge -- 4.2 Cloud Tier -- 5 Real-Time IoT Application: Predictive Maintenance of Industrial Motor -- 6 Building SDP for Predictive Maintenance Application -- 6.1 Proposed Serverless Data Pipelines -- 6.1.1 Building an Anomaly Detection Model -- 6.2 SDP Using AWS and Microsoft Azure -- 7 Experiments and Results -- 7.1 Performance Metrics -- 7.2 Experimental Setup -- 7.3 Results and Discussions -- 8 Conclusions -- References -- Integration of Predictive Analytics and Cloud Computing for Mental Health Prediction -- 1 Introduction -- 2 Method of Approach -- 2.1 Overview of the Subject -- 2.1.1 Supervised Learning -- 2.1.2 Unsupervised Learning -- 2.2 Selection of Papers -- 2.3 Literature Search Strategy -- 2.4 Study Selection -- 2.5 Data Extraction and Analysis -- 3 Introduction to Mental Health Research -- 3.1 Machine Learning in Big Data -- 3.2 Deep Learning in Healthcare -- 3.3 Natural Language Processing -- 4 The Pipeline of Data Flows from the Sensors to the Algorithmic Approach -- 4.1 Sensor Data -- 4.2 Extraction of Features -- 4.3 Designing the Behavioural Markers -- 4.4 Clinical Target -- 5 Cloud Computing -- 5.1 Architecture of Cloud Computing -- 5.2 Benefits of Cloud Computing in the Healthcare Industry -- 5.3 Cloud Computing as a Solution to Mental Health Issues -- 6 Review of Personal Sensing Research -- 7 Result of the Research -- 7.1 Limitations of the Study Done on the Algorithms to Detect Mental Health -- 7.2 Results Based on iCBT Test -- 8 Discussion -- 9 Conclusion -- References -- Impact of 5G Technologies on Cloud Analytics -- 1 Introduction -- 2 Self-Organizing Next Generation Network Data Analytics in the Cloud -- 2.1 What Is Network Data Analytics? -- 2.2 Benefits of Network Data Analytics. 2.3 The Best Uses of Network Data Analytics -- 2.4 The Near Future -- 2.5 The Opportunities -- 3 Intelligent 5G Network Estimation Techniques in the Cloud -- 3.1 Network Estimation Technique -- 3.2 Literature Review -- 4 5G-cloud Integration: Intelligent Security Protocol and Analytics -- 4.1 Scope -- 4.2 5G Cloud Threat -- 4.3 5G-Cloud Integration -- 4.4 Advantages of Security Capabilities -- 5 5G, Fog and Edge Based Approaches for Predictive Analytics -- 5.1 Introduction -- 5.2 Literature Review -- 6 5G and Beyond in Cloud, Edge, and Fog Computing -- 6.1 Edge Computing -- 6.2 Cloud Computing -- 6.3 5G and Beyond -- 7 AI-Enabled Next Generation 6G Wireless Communication -- 7.1 Computation Efficiency and Accuracy -- 7.2 Hardware Development -- 7.3 Types 6 G Wireless Communication -- 7.4 6G Wireless Access Use Case -- References -- IoT Based ECG-SCG Big Data Analysis Framework for Continuous Cardiac Health Monitoring in Cloud Data Centers -- 1 Introduction -- 2 Related Work -- 3 Proposed Cardiac Big Data Analysis Framework -- 3.1 ECG/SCG Data Collection Framework -- 3.2 Data Processing and Analysis Framework -- 3.3 MapReduce Based Cardiac Big Data Processing Model -- 4 Evaluation Results -- 5 Conclusion and Future Works -- References -- A Workload-Aware Data Placement Scheme for Hadoop-Enabled MapReduce Cloud Data Centers -- 1 Introduction -- 2 Related Works -- 3 Problem Description -- 4 Proposed Protocol -- 4.1 System Model -- 5 Problem Formulation -- 5.1 Network Model -- 5.2 Task Processing Model -- 5.3 Workload Distribution -- 6 Data Locality Problem -- 7 Conclusion and Future Works -- References -- 5G Enabled Smart City Using Cloud Environment -- 1 Introduction -- 2 Technologies Used to Build the Smart City -- 2.1 Edge and Fog Computing -- 2.2 What Price Does 5G Provide for Fog Computing? -- 2.3 Cloud Computing -- 2.4 Internet of Things. 3 SmartCity Architecture. |
| Record Nr. | UNINA-9910635395503321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Predictive analytics in cloud, fog, and edge computing : perspectives and practices of Blockchain, IoT, and 5G / / edited by Hiren Kumar Thakkar [and three others]
| Predictive analytics in cloud, fog, and edge computing : perspectives and practices of Blockchain, IoT, and 5G / / edited by Hiren Kumar Thakkar [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (252 pages) |
| Disciplina | 621.38456 |
| Soggetto topico |
Blockchains (Databases)
5G mobile communication systems Cloud computing |
| ISBN | 3-031-18034-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgement -- Contents -- Collaboration of IoT and Cloud Computing Towards HealthcareSecurity -- 1 Introduction -- 2 Inspiration -- 3 Related Work and Background -- 4 Cloud Computing Deployment Models -- 4.1 Public Internet -- 4.2 Corporate Cloud -- 4.3 Cloud Hybrid -- 4.4 Cloud Provider -- 5 Utility Computing Service Models -- 5.1 Software as a Service (SaaS) -- 5.2 Infrastructure as a Service (IaaS) -- 5.3 Platform as a Service (PaaS) -- 6 Security Issues -- 7 Threats in Cloud Computing -- 7.1 Compromised Identities and Broken Security -- 7.2 Data Infringement -- 7.3 Hacked Frontier and APIs -- 7.4 Manipulated System Vulnerabilities -- 7.5 Permanent Data Loss -- 7.6 Inadequate Assiduity -- 7.7 Cloud Service Inattention -- 7.8 DoS Attacks -- 7.9 Security Challenges in Cloud Infrastructure -- 7.9.1 Security Challenges -- 7.9.2 Challenges of Deployed Models -- 7.9.3 Resource Pooling -- 7.9.4 Unencrypted Data -- 7.9.5 Identity Management and Authentication -- 7.9.6 Network Issues -- 7.10 Point at Issue in the IoT Health Care Framework -- 7.10.1 Reliability -- 7.10.2 Discretion -- 7.10.3 Solitude -- 7.10.4 Unintended Efforts -- 7.11 Challenges -- 7.11.1 Security -- 7.11.2 Confidentiality -- 7.11.3 Assimilation -- 7.11.4 Business Illustration -- 7.12 Dispensing Refined Patient Supervision -- 7.13 Character of IoT in Healthcare -- 7.14 Conclusion -- 7.15 Future Work -- References -- Robust, Reversible Medical Image Watermarking for Transmission of Medical Images over Cloud in Smart IoT Healthcare -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 3.1 EHR Insertion (Embedding) and Retrieval (Extraction) -- 3.2 EHR Encryption and Decryption -- 4 Experimental Results and Discussion -- 5 Conclusions -- References -- The Role of Blockchain in Cloud Computing -- 1 Blockchain -- 1.1 Introduction -- 1.2 Characteristics.
1.2.1 Immutability -- 1.2.2 Distributed -- 1.2.3 Enhanced Security -- 1.2.4 Distributed Ledgers -- 1.2.5 Faster Settlement -- 1.2.6 Working of Blockchain -- 1.3 Major Implementations -- 1.3.1 Cryptocurrencies -- 1.3.2 Smart Contracts -- 1.3.3 Monetary Services -- 1.3.4 Games -- 1.4 Blockchain Types -- 1.5 There Are Mainly 4 Types of Blockchain as Shown in Table 1 -- 1.5.1 Public Blockchain Networks -- 1.5.2 Exclusive Blockchain Networks -- 1.5.3 Hybrid Blockchain Networks -- 1.5.4 Consortium Networks -- 1.6 Advantages -- 1.6.1 Secure -- 1.6.2 There Will Be No Intervention from Third Parties -- 1.6.3 Safe Transactions -- 1.6.4 Automation -- 1.7 Disadvantages -- 1.7.1 High Implementation Cost -- 1.7.2 Incompetency -- 1.7.3 Private Keys -- 1.7.4 Storage Capacity -- 2 Cloud Computing -- 2.1 What Is Cloud Computing? -- 2.2 Deployment Models in Cloud -- 2.2.1 Public Cloud -- 2.2.2 Private Cloud -- 2.2.3 Hybrid Cloud -- 2.2.4 Community Cloud -- 2.3 Implementations of Cloud Computing -- 2.3.1 Web Based Services -- 2.3.2 Software as a Service -- 2.3.3 Infrastructure as a Service -- 2.3.4 Platform as a Service -- 2.4 Comparison of Cloud Computing Model with Traditional Model -- 2.4.1 Persistency -- 2.4.2 Automation -- 2.4.3 Cost -- 2.4.4 Security -- 2.5 Advantages of Cloud Computing -- 2.5.1 Cost Efficiency -- 2.5.2 Backup and Recovery -- 2.5.3 Integration of Software -- 2.5.4 Information Availability -- 2.5.5 Deployment -- 2.5.6 Easier Scale for Services and Delivery of New Services -- 2.6 Challenges of Cloud Computing -- 2.6.1 Technical Problems -- 2.6.2 Certainty -- 2.6.3 Vulnerable Attacks -- 2.6.4 Suspension -- 2.6.5 Inflexibility -- 2.6.6 Lack of Assistance -- 2.7 Integration of Cloud Computing with Block Chain -- 2.7.1 The Advantages of Combining Cloud and Blockchain Technology -- 2.7.2 Blockchain Support for Cloud Computing. 2.7.3 Deduplication of Data in the Cloud with Blockchain -- 2.7.4 Access Control Based on Blockchain in Cloud -- References -- Analysis and Prediction of Plant Growth in a Cloud-Based Smart Sensor Controlled Environment -- 1 Introduction -- 2 Literature Survey -- 3 IoT in Greenhouse -- 3.1 Architecture -- 3.2 Cloud Implementation -- 3.3 Hardware Components (Fig. 2) -- 4 System Overview -- 4.1 Dataset -- 4.2 Data Preprocessing -- 4.3 LightGBM -- 4.4 Training and Building the Model -- 5 Results and Explanation -- 6 Conclusion -- References -- Cloud-Based IoT Controlled System Model for Plant DiseaseMonitoring -- 1 Introduction -- 2 Literature Survey -- 3 IoT Controlled Device -- 4 Cloud Architecture -- 5 Methodology -- 5.1 HOG Filter -- 6 Experimental Analysis -- 6.1 Analysis Using Artificial Neural Network -- 6.2 Analysis Using Convolutional Neural Network -- 7 Conclusion -- References -- Design and Usage of a Digital E-Pharmacy Application Framework -- 1 Introduction -- 2 Literature Survey -- 3 Utilization of Cloud in Health Care -- 4 Redefining E-Pharmacy Domain -- 5 Impact of Cloud Computing in Pharmacy -- 6 Model Design and Implementation -- 7 Basic Structure of the Cloud Based E-Pharmacy Application -- 8 Security Provided by the Application -- 8.1 XSS Security (Cross Site Scripting) -- 8.2 CSRF Token (Cross Site Request Forgery) -- 8.3 SQL Injection Security -- 8.4 User Upload Security -- 9 Results and Discussion -- 10 Important Features of the Application -- 11 Critical Goals of the Application -- 12 Benefits of the Model -- 13 Summary/Conclusion -- References -- Serverless Data Pipelines for IoT Data Analytics: A Cloud Vendors Perspective and Solutions -- 1 Introduction -- 1.1 Motivation -- 1.2 Contributions -- 2 Background -- 2.1 Internet of Things -- 2.2 Serverless Data Pipelines for IoT Data Processing -- 3 Literature Survey. 4 Cloud Service Providers (CSP) and IoT Solutions -- 4.1 Edge Tier -- 4.1.1 Comparison of AWS IoT Greengrass and Azure IoT Edge -- 4.2 Cloud Tier -- 5 Real-Time IoT Application: Predictive Maintenance of Industrial Motor -- 6 Building SDP for Predictive Maintenance Application -- 6.1 Proposed Serverless Data Pipelines -- 6.1.1 Building an Anomaly Detection Model -- 6.2 SDP Using AWS and Microsoft Azure -- 7 Experiments and Results -- 7.1 Performance Metrics -- 7.2 Experimental Setup -- 7.3 Results and Discussions -- 8 Conclusions -- References -- Integration of Predictive Analytics and Cloud Computing for Mental Health Prediction -- 1 Introduction -- 2 Method of Approach -- 2.1 Overview of the Subject -- 2.1.1 Supervised Learning -- 2.1.2 Unsupervised Learning -- 2.2 Selection of Papers -- 2.3 Literature Search Strategy -- 2.4 Study Selection -- 2.5 Data Extraction and Analysis -- 3 Introduction to Mental Health Research -- 3.1 Machine Learning in Big Data -- 3.2 Deep Learning in Healthcare -- 3.3 Natural Language Processing -- 4 The Pipeline of Data Flows from the Sensors to the Algorithmic Approach -- 4.1 Sensor Data -- 4.2 Extraction of Features -- 4.3 Designing the Behavioural Markers -- 4.4 Clinical Target -- 5 Cloud Computing -- 5.1 Architecture of Cloud Computing -- 5.2 Benefits of Cloud Computing in the Healthcare Industry -- 5.3 Cloud Computing as a Solution to Mental Health Issues -- 6 Review of Personal Sensing Research -- 7 Result of the Research -- 7.1 Limitations of the Study Done on the Algorithms to Detect Mental Health -- 7.2 Results Based on iCBT Test -- 8 Discussion -- 9 Conclusion -- References -- Impact of 5G Technologies on Cloud Analytics -- 1 Introduction -- 2 Self-Organizing Next Generation Network Data Analytics in the Cloud -- 2.1 What Is Network Data Analytics? -- 2.2 Benefits of Network Data Analytics. 2.3 The Best Uses of Network Data Analytics -- 2.4 The Near Future -- 2.5 The Opportunities -- 3 Intelligent 5G Network Estimation Techniques in the Cloud -- 3.1 Network Estimation Technique -- 3.2 Literature Review -- 4 5G-cloud Integration: Intelligent Security Protocol and Analytics -- 4.1 Scope -- 4.2 5G Cloud Threat -- 4.3 5G-Cloud Integration -- 4.4 Advantages of Security Capabilities -- 5 5G, Fog and Edge Based Approaches for Predictive Analytics -- 5.1 Introduction -- 5.2 Literature Review -- 6 5G and Beyond in Cloud, Edge, and Fog Computing -- 6.1 Edge Computing -- 6.2 Cloud Computing -- 6.3 5G and Beyond -- 7 AI-Enabled Next Generation 6G Wireless Communication -- 7.1 Computation Efficiency and Accuracy -- 7.2 Hardware Development -- 7.3 Types 6 G Wireless Communication -- 7.4 6G Wireless Access Use Case -- References -- IoT Based ECG-SCG Big Data Analysis Framework for Continuous Cardiac Health Monitoring in Cloud Data Centers -- 1 Introduction -- 2 Related Work -- 3 Proposed Cardiac Big Data Analysis Framework -- 3.1 ECG/SCG Data Collection Framework -- 3.2 Data Processing and Analysis Framework -- 3.3 MapReduce Based Cardiac Big Data Processing Model -- 4 Evaluation Results -- 5 Conclusion and Future Works -- References -- A Workload-Aware Data Placement Scheme for Hadoop-Enabled MapReduce Cloud Data Centers -- 1 Introduction -- 2 Related Works -- 3 Problem Description -- 4 Proposed Protocol -- 4.1 System Model -- 5 Problem Formulation -- 5.1 Network Model -- 5.2 Task Processing Model -- 5.3 Workload Distribution -- 6 Data Locality Problem -- 7 Conclusion and Future Works -- References -- 5G Enabled Smart City Using Cloud Environment -- 1 Introduction -- 2 Technologies Used to Build the Smart City -- 2.1 Edge and Fog Computing -- 2.2 What Price Does 5G Provide for Fog Computing? -- 2.3 Cloud Computing -- 2.4 Internet of Things. 3 SmartCity Architecture. |
| Record Nr. | UNISA-996547962603316 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Predictive data security using AI : insights and issues of Blockchain, IoT, and DevOps / / Hiren Kumar Thakkar, Mayank Swarnkar, Robin Singh Bhadoria, editors
| Predictive data security using AI : insights and issues of Blockchain, IoT, and DevOps / / Hiren Kumar Thakkar, Mayank Swarnkar, Robin Singh Bhadoria, editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (222 pages) |
| Disciplina | 006.3 |
| Collana | Studies in computational intelligence |
| Soggetto topico |
Artificial intelligence
Computer security |
| ISBN | 981-19-6290-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- About the Editors -- A Comprehensive Study of Security Aspects in Blockchain -- 1 Introduction -- 2 Characteristics of Blockchain Technology -- 3 Working of Blockchain -- 4 Analysis of Security in Blockchain -- 4.1 Risks to Blockchain -- 4.2 Attacks on Blockchain -- 5 Security Enhancements -- 6 Applications of Blockchain -- 7 Trade-Offs and Challenges of Blockchain Technology -- 8 Conclusion -- References -- An Exploration Analysis of Social Media Security -- 1 Introduction to Social Media Security and Its Evolution -- 2 Important Issues Involving Security for Social Media -- 2.1 Privacy of Data -- 2.2 Data Mining -- 2.3 Virus and Malware Attacks -- 2.4 Legal Issues -- 3 Risks and Challenges of Social Media Security -- 3.1 Information Revelation -- 3.2 Location Spillage -- 3.3 Cyberbullying and Cyberstalking -- 3.4 Cyber Terrorism -- 3.5 Reputation Misfortune -- 3.6 Identity Theft -- 4 Social Media Networks Security Solutions -- 4.1 Watermarking -- 4.2 Steganalysis -- 4.3 Digital Oblivion -- 4.4 Storage Encryption -- 4.5 Detection of Malware and Phishing -- 4.6 Prediction of Cyberattacks Through Monitoring Social Media -- 4.7 Time Lag-Based Modelling for Software Vulnerability Exploitation Process -- 4.8 Session Hijacking Counter Measures -- 4.9 Privacy Set-Up on Social Networking Sites -- 5 Conclusion -- References -- A Pragmatic Analysis of Security Concerns in Cloud, Fog, and Edge Environment -- 1 Introduction to Cloud Computing -- 2 Introduction to Fog Computing -- 3 Introduction to Edge Computing -- 4 Security Threats of Cloud Fog and Edge Computing -- 5 Potential Solution of Cloud Fog and Edge Computing -- 6 Conclusion and Future Scope -- References -- Secure Information and Data Centres: An Exploratory Study -- 1 Introduction -- 1.1 History of Data Centre.
1.2 Importance of Data Centres in a Business Environment -- 2 Core Parts of a Data Centre -- 2.1 Network Infrastructure -- 2.2 Storage Infrastructure -- 2.3 Server Infrastructure -- 2.4 Computing Resources -- 2.5 Categories of Data Centre Facilities -- 3 Requirements of a Modern Data Centre -- 3.1 Abundant, Reliable Power -- 3.2 Cool Conditions -- 3.3 Physical and Virtual Security Measures -- 4 Tiered Data Centres -- 4.1 Uptime Institute -- 5 Challenges in Data centre Networking -- 5.1 Data Security -- 5.2 Power Management -- 5.3 Capacity Planning -- 5.4 The Internet of Things (IoT) -- 5.5 Mobile Enterprise -- 5.6 Real-Time Reporting -- 5.7 Balancing Cost Controls with Efficiency -- 6 Threats Faced by Data Centres in India -- 6.1 Inadequate Cognizance of Assets -- 6.2 Disproportionate Energy Exhaustion -- 6.3 Inefficient Capacity Planning -- 6.4 Unfortunate Staff Productivity -- 6.5 Long Recovery Periods -- 6.6 Growing Security Concerns -- 7 Security Threats of Data Centre -- 7.1 Classes of Data Centre Security -- 7.2 Who Needs Data Centre Security? -- 8 Cybersecurity Threats to Heed -- 8.1 Phishing Engineering Attacks -- 8.2 Ransomware -- 8.3 Cyberattacks Against Hosted Services -- 8.4 IoT-Based Attacks -- 8.5 Internal Attacks -- 8.6 Unpatched Security Susceptibility and Bugs -- 9 How to Keep Data Centre Secure -- 10 How to Curb These Attacks -- 10.1 Secure Your Hardware -- 10.2 Encrypt and Backup Data -- 10.3 Create a Security-Focused Workplace Culture -- 10.4 Invest in Cybersecurity Insurance -- 10.5 Physical Security -- 10.6 Virtual Security -- 11 How to Secure Data Centres Against or After Cyberattacks -- 11.1 Securing Different Regions Through Network Segmentation -- 11.2 Moving Beyond Segmentation to Cyber -- 11.3 Advanced Attacks and Mature Attacks -- 11.4 Behavioural -- 11.5 Preempt the Silos. 12 Checklist to Help with Security Arrangements -- 13 Benefits of Cybersecurity -- 14 Conclusion -- References -- Blockchain-Based Secure E-voting System Using Aadhaar Authentication -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 3.1 System Architecture -- 4 Implementation Details -- 5 Security Analysis of Proposed System -- 6 Comparison with Existing Techniques -- 7 Conclusion and Future Scope -- References -- DevOps Tools: Silver Bullet for Software Industry -- 1 Introduction -- 1.1 Background -- 2 DevOps Life Cycle -- 2.1 Continuous Development -- 2.2 Continuous Integration -- 2.3 Continuous Testing -- 2.4 Continuous Deployment -- 2.5 Continuous Monitoring -- 2.6 Continuous Feedback -- 2.7 Continuous Operations -- 3 DevOps Tools -- 3.1 Code -- 3.2 Build -- 3.3 Test -- 3.4 Delivery -- 3.5 Deployment -- 3.6 Monitor -- 4 DevOps in Industry and Education -- 5 Conclusion and Future Perspective -- References -- Robust and Secured Reversible Data Hiding Approach for Medical Image Transmission over Smart Healthcare Environment -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 3.1 Watermark Embedding and Extraction -- 3.2 Watermark Encryption and Decryption -- 4 Experimental Results and Discussion -- 4.1 Imperceptibility Test -- 4.2 Robustness Test -- 4.3 Security Test -- 4.4 Computational Cost -- 5 Conclusions -- References -- Advancements in Reversible Data Hiding Techniques and Its Applications in Healthcare Sector -- 1 Introduction -- 2 Methods of Secure Communication -- 2.1 Steganography -- 2.2 Reversible Data Hiding (RDH) -- 2.3 Digital Watermarking -- 3 Related Work -- 3.1 Efficiency Parameters -- 3.2 Related Works on Reversible Data Hiding -- 3.3 Related Works on Reversible Watermarking -- 4 Medical Image Datasets for the Research Work -- 5 Research Challenges -- 6 Conclusion -- References -- Security Issues in Deep Learning. 1 Introduction -- 1.1 Implementations of Deep Learning -- 2 Background -- 2.1 Deep Learning -- 2.2 Deep Neural Networks (DNNs) -- 2.3 Artificial Intelligence -- 2.4 DNNs Properties -- 2.5 Strategies for Secrecy for In-Depth Learning -- 3 In-Depth Reading of Private Data Frames -- 3.1 Shokri and Shmatikov -- 3.2 SecureML -- 3.3 Google -- 3.4 CryptoNets -- 3.5 MiniONN -- 3.6 Chameleon -- 3.7 DeepSecure -- 4 Deep Learning Attack -- 4.1 Trained Model -- 4.2 Inputs and Prediction Results -- 5 Attack that Destroys Example -- 5.1 Introduction of Model Extraction Attack -- 5.2 Adversary Model -- 5.3 Alternative Released Information -- 6 Possible Attacks of Example -- 6.1 Introducing the Model Inversion Attack -- 6.2 Suspected Membership Attack -- 7 Poison Attack -- 7.1 Attack Assaults on Ordinary Supervised Analysis (LR) -- 7.2 Poisoning Assaults in Conventional Unsupervised Learning -- 7.3 Poison Attack on Deep Learning -- 7.4 Poison Assault on Strengthening Training -- 8 Adversarial Attack -- 8.1 How to Attack Enemies -- 9 Unlock Problems -- 10 Conclusion -- References -- CNN-Based Models for Image Forgery Detection -- 1 Introduction -- 2 Theoretical Background -- 3 Dataset Description -- 4 Methodology -- 4.1 Data Pre-processing -- 4.2 Training Models -- 4.3 Workflow of the Proposed CNN Model -- 5 Result and Analysis -- 5.1 Hyper-parameters -- 5.2 Pseudocode -- 5.3 Evaluation Metrics -- 5.4 Training and Validation Loss Curve -- 5.5 Confusion Matrix -- 6 Conclusion and Future Scope -- References -- Malicious URL Detection Using Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Overview of Principles of Detecting Malicious URLs -- 3.1 Blacklisting or Heuristic Approaches -- 3.2 Machine Learning Approaches -- 4 Datasets -- 5 Feature Extraction -- 5.1 URL-Based Lexical Features -- 5.2 DNS-Based Features -- 5.3 Webpage Content-Based Features. 6 Machine Learning Algorithms for Malicious URL Detection -- 7 Practical Issues and Open Problems -- 8 Conclusion -- References. |
| Record Nr. | UNINA-9910633913903321 |
| Singapore : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||