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Applications of Artificial Intelligence in COVID-19 / / edited by Sachi Nandan Mohanty, Shailendra K. Saxena, Suneeta Satpathy, Jyotir Moy Chatterjee
| Applications of Artificial Intelligence in COVID-19 / / edited by Sachi Nandan Mohanty, Shailendra K. Saxena, Suneeta Satpathy, Jyotir Moy Chatterjee |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (593 pages) |
| Disciplina | 610.285 |
| Collana | Medical Virology: From Pathogenesis to Disease Control |
| Soggetto topico |
Virology
Epidemiology Artificial intelligence Artificial Intelligence COVID-19 Intel·ligència artificial en medicina |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-15-7317-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1.Comprehensive Claims of AI for Healthcare Applications-Coherence towards COVID-19 -- Chapter 2. Artificial Intelligence based systems for combating COVID-19 -- Chapter 3. Artificial intelligence mediated medical diagnosis of COVID-19 -- Chapter 4. AI combined with medical imaging enables rapid diagnosis for COVID-19 -- Chapter 5. Role of Artificial Intelligence in COVID-19 prediction based on Statistical Methods -- Chapter 6. Data Driven symptom Analysis and Location Prediction Model for Clinical Health Data Processing and Knowledgebase Development for COVID 19 -- Chapter 7. A decision support System using Rule based Expert System For COVID -19 Prediction and Diagnosis -- Chapter 8. A Predictive Mechanism to Intimate the Danger of Infection via nCOVID-19 through Unsupervised Learning -- Chapter 9. AI-enabled prognosis technologies for SARS Co-2. Chapter 10. Intelligent agent Based Case Base Reasoning Systems Build Knowledge Representation in COVID-19 analysis of Recovery, Infectious Patients -- Chapter 11. Epidemic Analysis of COVID 19 Using Machine Learning -- Chapter 12. Machine learning application in COVID-19 drug development -- Chapter 13. COVID 19 Epidemic Analysis Using Linear and Polynomial Regression Approach -- Chapter 14. Prediction & Analysis of outbreak of COVID-19 Pandemic Using Machine Learning -- Chapter 15. Predictive Risk Analysis by using Machine Learning during Covid-19 -- Chapter 16. Analysis and Validation of Risk Prediction by Stochastic Gradient Boosting Along With Recursive Feature Elimination for COVID-19 -- Chapter 17. Artificial intelligence in mental healthcare during COVID-19 pandemic -- Chapter 18. Effect of Covid-19 on Autism Spectrum Disorder: Prognosis, diagnosis and therapeutics based On AI -- Chapter 19. Use of mobile phone apps for contact tracing to control the COVID-19 pandemic: A Literature Review -- Chapter 20. Role of IoT and Social Networking in Mental Healthcare of Transgender Community in Covid-19 Pandemic -- Chapter 21. TECHNOLOGY ACCEPTANCE AND USE OF IOT DURING COVID 19 PANDEMIC-CASE STUDY OF HEALTH SECTOR IN INDIA. Chapter 22. Artificial Intelligence – The Strategies used in COVID-19 for Diagnosis -- Chapter 23. Impact of Isolation and Quarantine on Covid-19 Patients and Potential Role of Technology in Mitigation -- Chapter 24. Impact of loneliness and Quarantine on COVID-19 patients with artificial intelligence applications -- Chapter 25. Can Technology fight the loneliness Lockdown: A study of factors Affecting Loneliness in NCR during COVID 19 -- Chapter 26. Psycho-economic Impact of Obligatory Job Switching during Covid-19 Pandemic: A Study of Hawkers in Bhubaneswar (India) -- Chapter 27. AI’s Role in Essential Commodities during a Pandemic Situation -- Chapter 28.Impact of COVID-19 on Manufacturing and Operational Ecosystem in India -- Chapter 29. Impact of Repatriated Migrants on the Production Possibility of Agricultural Sector owing to Covid: A Study on the basis of Inferential Statistics -- Chapter 30. Nicotine in Covid-19: Friend or Foe”;? -- Chapter 31. Artificial Intelligence in Covid’19: Application and Legal Conundrums. |
| Record Nr. | UNINA-9910502987803321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Big Data Analytics and Computing for Digital Forensic Investigations [[electronic resource]]
| Big Data Analytics and Computing for Digital Forensic Investigations [[electronic resource]] |
| Pubbl/distr/stampa | Milton, : CRC Press LLC, 2020 |
| Descrizione fisica | 1 online resource (235 pages) : illustrations |
| Disciplina | 363.25968 |
| Altri autori (Persone) |
SatpathySuneeta
MohantySachi Nandan |
| Soggetto topico | Computer crimes - Investigation |
| ISBN |
1-00-302474-2
1-000-04503-X 1-000-04505-6 1-003-02474-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910794188703321 |
| Milton, : CRC Press LLC, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Big Data Analytics and Computing for Digital Forensic Investigations
| Big Data Analytics and Computing for Digital Forensic Investigations |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Milton, : CRC Press LLC, 2020 |
| Descrizione fisica | 1 online resource (235 pages) : illustrations |
| Disciplina |
363.25968
005.7 |
| Altri autori (Persone) |
SatpathySuneeta
MohantySachi Nandan |
| Soggetto topico | Computer crimes - Investigation |
| ISBN |
1-00-302474-2
1-000-04503-X 1-000-04505-6 1-003-02474-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- Editors -- Contributors -- Chapter 1 Introduction to Digital Forensics -- 1.1 Digital Forensics Overview -- 1.1.1 Definitions of Digital Forensics -- 1.1.2 The 3A's of Digital Forensics Methodology -- 1.1.3 The History of Digital Forensics -- 1.1.4 The Objectives of Digital Forensics -- 1.2 Digital Evidence -- 1.2.1 Active Data -- 1.2.2 Archival Data -- 1.2.3 Latent Data -- 1.2.4 Residual Data -- 1.3 Branches of Digital Forensics -- 1.3.1 Computer Forensics -- 1.3.2 Network Forensics -- 1.3.3 Software Forensics -- 1.3.4 Mobile Forensics -- 1.3.5 Memory Forensics -- 1.3.6 Malware Forensics -- 1.3.7 Database Forensics -- 1.3.8 Social Network Forensics -- 1.3.9 Anti-Forensics -- 1.3.10 Cloud Forensics -- 1.3.11 Bit Coin Forensics -- 1.3.12 Big Data Forensics -- 1.4 Phases of Forensic Investigation Process -- 1.4.1 Readiness -- 1.4.2 Identification -- 1.4.3 Collection -- 1.4.4 Analysis -- 1.4.5 Presentation -- 1.4.5.1 Chain of Custody -- 1.5 Conclusion -- References -- Chapter 2 Digital Forensics and Digital Investigation to Form a Suspension Bridge Flanked by Law Enforcement, Prosecution, and Examination of Computer Frauds and Cybercrime -- 2.1 Forensic Science and Digital Forensics -- 2.2 Digital Forensics -- 2.2.1 Digital Evidence -- 2.3 Segments of Digital Forensics -- 2.3.1 Preparation -- 2.3.1.1 An Investigative Plan -- 2.3.1.2 Training and Testing -- 2.3.1.3 Equipment -- 2.4 Compilation -- 2.4.1 Evidence Search and Collection -- 2.4.2 Data Recovery -- 2.4.3 Assessment -- 2.4.4 Post-Assessment -- 2.5 Stepladder of Digital Forensic Investigation Model -- 2.5.1 Recognition of Sources of Digital Evidence -- 2.5.2 Conservation of Evidentiary Digital Data -- 2.5.3 Mining of Evidentiary Data from Digital Media Sources.
2.5.4 Recording of Digital Evidence in Form of Report -- 2.6 Disciplines of Digital Forensics -- 2.6.1 Computer Forensics -- 2.6.2 Network Forensics -- 2.6.3 Software Forensics -- 2.7 Digital Crime Investigative Tools and Its Overview -- 2.7.1 EnCase Toolkit -- 2.7.2 Forensic Toolkit -- 2.7.3 SafeBack Toolkit -- 2.7.4 Storage Media Archival Recovery Toolkit -- 2.8 Taxonomy of Digital Crime Investigative Tools -- 2.8.1 Functionalities of Digital Investigative Tool Can Be Grouped under -- 2.8.1.1 Replica of the Hard Drive -- 2.8.1.2 Investigational Analysis -- 2.8.1.3 Presentation -- 2.8.1.4 Documentary Reporting -- 2.9 Boundaries and Commendations of Digital Crime Investigative Tools -- 2.10 Conclusion -- References -- Chapter 3 Big Data Challenges and Hype Digital Forensic: A Review in Health Care Management -- 3.1 Introduction -- 3.2 Big Data for Health Care -- 3.3 Big Data for Health Care Strategy Making -- 3.3.1 Pattern Developments -- 3.3.2 Evaluation and Interpretation -- 3.3.3 Result and Usage -- 3.4 Opportunity Generation and Big Data in Health Care Sector -- 3.4.1 Value Creation -- 3.5 Big Data and Health Care Sector Is Meeting Number of Challenges -- 3.5.1 Volume -- 3.5.2 Variety -- 3.5.3 Velocity and Variety -- 3.5.4 Data Findings -- 3.5.5 Privacy -- 3.6 Digitalized Big Data and Health Care Issues -- 3.6.1 Effective Communication Safely Data Storage -- 3.6.2 Availability of Data for General People -- 3.6.3 Logical Data -- 3.6.4 Effective Communication of Health Care Data -- 3.6.5 Data Capturing -- 3.6.5.1 Alignment of Data Sources -- 3.6.5.2 Algorithm of Data for Suitable Analysis -- 3.6.6 Understanding the Output and Accessibility towards the End Users -- 3.6.6.1 Privacy and Secrecy -- 3.6.6.2 Governance and Ethical Standards -- 3.6.6.3 Proper Audit -- 3.7 Precautionary Attempt for Future Big Data Health Care -- 3.7.1 Data Secrecy. 3.7.2 Web-Based Health Care -- 3.7.3 Genetically and Chronic Disease -- 3.8 Forensic Science and Big Data -- 3.9 Types of Digital Forensics -- 3.9.1 Digital Image Forensics -- 3.9.2 Drawn Data for the Starting of a Process -- 3.9.3 Big Data Analysis -- 3.9.3.1 Definition -- 3.9.3.2 Interpretation -- 3.9.3.3 Big Data Framework -- 3.9.3.4 Forensic Tool Requirement for the Huge Data in Health Care -- 3.10 Digital Forensics Analysis Tools -- 3.10.1 AIR (Automated Image and Rest Store) -- 3.10.2 Autopsy -- 3.10.3 Window Forensic Tool Chart -- 3.10.4 Digital Evidence and Forensic Tool Kit -- 3.10.5 EnCase -- 3.10.6 Mail Examiner -- 3.10.7 FTK -- 3.10.8 Bulk Extractors -- 3.10.9 Pre-Discover Forensic -- 3.10.10 CAINE -- 3.10.11 Xplico -- 3.10.12 X-Ways Forensic -- 3.10.13 Bulk Extractor -- 3.10.14 Digital Forensics Framework -- 3.10.15 Oxygen Forensics -- 3.10.16 Internet Evidence Finder -- 3.11 Some Other Instruments for Big Data Challenge -- 3.11.1 MapReduce Technique -- 3.11.2 Decision Tree -- 3.11.3 Neural Networks -- 3.12 Conclusion -- References -- Chapter 4 Hadoop Internals and Big Data Evidence -- 4.1 Hadoop Internals -- 4.2 The Hadoop Architectures -- 4.2.1 The Components of Hadoop -- 4.3 The Hadoop Distributed File System -- 4.4 Data Analysis Tools -- 4.4.1 Hive -- 4.4.2 HBase -- 4.4.3 Pig -- 4.4.4 Scoop -- 4.4.5 Flume -- 4.5 Locating Sources of Evidence -- 4.5.1 The Data Collection -- 4.5.2 Structured and Unstructured Data -- 4.5.3 Data Collection Types -- 4.6 The Chain of Custody Documentation -- 4.7 Conclusion -- Bibliography -- Chapter 5 Security and Privacy in Big Data Access Controls -- 5.1 Introduction -- 5.1.1 Big Data Is Not Big? -- 5.2 Big Data Challenges to Information Security and Privacy -- 5.3 Addressing Big Data Security and Privacy Challenges: A Proposal -- 5.4 Data Integrity Is Not Data Security! -- 5.4.1 What Vs Why?. 5.4.2 Data Integrity: Process Vs State -- 5.4.3 Integrity Types -- 5.4.3.1 Physical Integrity -- 5.4.3.2 Logical Integrity -- 5.4.3.3 Entity Integrity -- 5.4.3.4 Referential Integrity -- 5.4.3.5 Domain Integrity -- 5.4.3.6 User-Defined Integrity -- 5.5 Infiltration Activities: Fraud Detection with Predictive Analytics -- 5.6 Case Study I: In a Secure Social Application -- 5.6.1 Overall System Architecture -- 5.6.2 Registration on the Platform -- 5.6.3 Sharing Content on the Platform -- 5.6.4 Accessing Content on the Platform -- 5.7 Case Study II -- 5.7.1 An Intelligent Intrusion Detection/Prevention System on a Software-Defined Network -- 5.7.2 The Code Reveals -- 5.7.3 Evaluation -- 5.8 Big Data Security: Future Directions -- 5.9 Final Recommendations -- References -- Chapter 6 Data Science and Big Data Analytics -- 6.1 Objective -- 6.2 Introduction -- 6.2.1 What Is Big Data? -- 6.2.2 What Is Data Science? -- 6.2.3 What Is Data Analytics? -- 6.2.3.1 Descriptive Analytics -- 6.2.3.2 Diagnostic Analytics -- 6.2.3.3 Predictive Analytics -- 6.2.3.4 Prescriptive Analytics -- 6.2.4 Data Analytics Process -- 6.2.4.1 Business Understanding -- 6.2.4.2 Data Exploration -- 6.2.4.3 Preprocessing -- 6.2.4.4 Modeling -- 6.2.4.5 Data Visualization -- 6.3 Techniques for Data Analytics -- 6.3.1 Techniques in Preprocessing Stage -- 6.3.1.1 Data Cleaning -- 6.3.1.2 Data Transformation -- 6.3.1.3 Dimensionality Reduction -- 6.3.2 Techniques in Modeling Stage -- 6.3.2.1 Regression -- 6.3.2.2 Classification -- 6.3.2.3 Clustering -- 6.3.2.4 Association Rules -- 6.3.2.5 Ensemble Learning -- 6.3.2.6 Deep Learning -- 6.3.2.7 Reinforcement Learning -- 6.3.2.8 Text Analysis -- 6.3.2.9 Cross-Validation -- 6.4 Big Data Processing Models and Frameworks -- 6.4.1 Map Reduce -- 6.4.2 Apache Frameworks -- 6.5 Summary -- References. Chapter 7 Awareness of Problems and Defies with Big Data Involved in Network Security Management with Revised Data Fusion-Based Digital Investigation Model -- 7.1 Introduction -- 7.2 Big Data -- 7.2.1 Variety -- 7.2.2 Volume -- 7.2.3 Velocity -- 7.2.4 Veracity -- 7.2.5 Value -- 7.3 Big Data and Digital Forensics -- 7.4 Digital Forensic Investigation and Its Associated Problem Statements -- 7.5 Relevance of Data Fusion Application in Big Data Digital Forensics and Its Investigation -- 7.6 Data Fusion -- 7.6.1 The JDL Practical Data Fusion Procedural Model -- 7.7 Revised Data Fusion-Based Digital Investigation Model for Digital Forensic and Network Threat Management -- 7.7.1 Data Collection and Preprocessing -- 7.7.2 Look-Up Table -- 7.7.3 Low-Level Fusion -- 7.7.4 Data Estimation Phase -- 7.7.5 High-Level Fusion -- 7.7.6 Decision-Level Fusion -- 7.7.7 Forensic Logbook -- 7.7.8 User Interface -- 7.8 Practicability and Likelihood of Digital Investigation Model -- 7.9 Conclusion and Future Work -- References -- Chapter 8 Phishing Prevention Guidelines -- 8.1 Phishing -- 8.1.1 Why Phishing Works -- 8.1.2 Phishing in Enterprise -- 8.2 Phishing Prevention Guidelines -- 8.2.1 Cyber Awareness and Hygiene -- 8.2.2 Phishing Prevention on Ground Level -- 8.2.3 Phishing Precautionary Measures at Enterprise Environs -- 8.2.4 Sturdy and Meticulous Web Development Is Recommended -- 8.2.5 Suggestive Measures for other Cybercrime -- 8.3 Implementation of Phishing Prevention Guidelines -- 8.4 Validation of Phishing Prevention Guidelines -- 8.5 Summary -- References -- Chapter 9 Big Data Digital Forensic and Cybersecurity -- 9.1 Introduction -- 9.2 Computer Frauds and Cybercrime -- 9.2.1 Tools Used in Cybercrime -- 9.2.2 Cybercrime Statistics for 2018-2019 -- 9.3 Taxonomy -- 9.4 Information Warfare -- 9.4.1 The Basic Strategies in Information Warfare. 9.4.2 Various Forms of Information Warfare. |
| Record Nr. | UNINA-9910975182203321 |
| Milton, : CRC Press LLC, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cloud security : techniques and applications / / Sirisha Potluri, Katta Subba Rao, Sachi Nandan Mohanty, editors
| Cloud security : techniques and applications / / Sirisha Potluri, Katta Subba Rao, Sachi Nandan Mohanty, editors |
| Pubbl/distr/stampa | Berlin ; ; Boston : , : De Gruyter, , [2021] |
| Descrizione fisica | 1 online resource (XX, 192 p.) |
| Disciplina | 004.6782 |
| Collana | De Gruyter series on smart computing applications |
| Soggetto topico |
Cloud computing
Computer security |
| ISBN | 3-11-073257-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Preface -- Acknowledgement -- Contents -- List of Abbreviations -- List of Contributors -- Cloud Security Concepts, Threats and Solutions: Artificial Intelligence Based Approach -- Addressing Security and Privacy in Cloud Computing: Blockchain as a Service -- Security and Privacy Preservation Model to Mitigate DDoS Attacks in Cloud -- A Secure Cloud Infrastructure towards Smart Healthcare: IoT Based Health Monitoring -- Internet of Cloud: Secure and Privacy Preserving Cloud Model with IoT Enabled Service -- Marketing analytics as a Service: Secure Cloud Based Automation Strategy -- Next Generation Cloud Security: State of the Art Machine Learning Model -- Secure Intelligent Framework for VANET: Cloud Based Transportation Model -- Cloud Manufacturing Service: A Secure and Protected Communication System -- Index |
| Record Nr. | UNINA-9910554217703321 |
| Berlin ; ; Boston : , : De Gruyter, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Creative Approaches Towards Development of Computing and Multidisciplinary IT Solutions for Society
| Creative Approaches Towards Development of Computing and Multidisciplinary IT Solutions for Society |
| Autore | Bijalwan Anchit |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (585 pages) |
| Disciplina | 004 |
| Altri autori (Persone) |
BennettRick
G. BJyotsna MohantySachi Nandan |
| Soggetto topico | Computer science |
| ISBN |
9781394272303
1394272308 9781394272280 1394272286 9781394272297 1394272294 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: Emerging Research in Next Generation Computing Like Cloud Computing, Cybersecurity, and Gaming -- Chapter 1 Deploying Virtual Desktop Infrastructure with Open-Source Platform for Higher Education -- 1.1 Introduction -- 1.2 Background -- 1.2.1 Cloud Computing -- 1.2.2 Virtualization -- 1.3 VDI Deployment Using CloudStack as a Private Cloud -- 1.4 Deploy at Information Technology Laboratories -- 1.5 Conclusion -- References -- Chapter 2 Enhancing Intrusion Detection Effectiveness Through an Enhanced Hierarchical Communication Architecture -- 2.1 Introduction -- 2.2 Related Works -- 2.3 Proposed Model -- 2.4 Analysis -- 2.5 Conclusion -- References -- Chapter 3 Enhanced SDN Security Using Mobile Agent -- 3.1 Introduction -- 3.1.1 Introduction to SDN -- 3.1.2 SDN Compared to Conventional Networking -- 3.2 Network Security in SDN -- 3.2.1 Vulnerabilities in SDN -- 3.2.2 Threats on SDN -- 3.3 Enhanced SDN Network Security Using Mobile Agent -- 3.3.1 Cloud Network Management Mobile Agent (CNMMA) Architecture Model -- 3.3.2 Mobile Agent Platform (MAP) -- 3.3.3 Network Management Mobile Agent -- 3.3.4 Mobile Agent Distributed Intrusion Detection System Framework (MA-DIDS) -- 3.3.4.1 IDS-Control Center -- 3.3.4.2 Mobile Agent SDN Control App -- 3.3.5 SDN Network Simulator -- 3.4 Conclusions -- References -- Chapter 4 Understanding the Impact and Implications of Emagnet and Pastebin in Cybersecurity -- 4.1 Introduction -- 4.1.1 Background of Emagnet and Pastebin -- 4.1.2 Importance of the Research -- 4.1.2.1 Research Questions -- 4.2 Literature Review -- 4.2.1 Evolution of Pastebin as a Platform for Hacker Exploits -- 4.2.1.1 Emagnet's Capabilities and Functionalities -- 4.3 Leaked Databases -- 4.4 Methodology -- 4.4.1 Emagnet -- 4.4.2 Key Features and Known Issues.
4.4.3 How Emagnet Works -- 4.4.4 Installation and Platform Requirements -- 4.4.5 Emagnet Usage Options -- 4.4.6 Key Features and Benefits -- 4.4.7 Our Review -- 4.4.8 Pastebin -- 4.4.9 Pastebin's Dark Side -- 4.4.10 The Need for Vigilance -- 4.4.11 Leveraging Authentic8 Flash Report -- 4.4.12 The Role of Silo for Research -- 4.4.13 Our Implementation Case Study -- 4.4.14 Our Review -- 4.5 Countermeasures and Best Practices -- 4.5.1 Strategies for Individuals and Organizations -- 4.5.2 Strengthening Password Security and Promoting 2FA -- 4.5.3 Responsible Vulnerability Disclosure -- 4.6 Recommendations and Future Directions -- 4.6.1 Developing Effective Policies -- 4.6.2 Enhancing Collaboration Between Stakeholders -- 4.6.3 Raising Awareness Among Users -- 4.6.4 Predicting Future Trends and Challenges -- 4.7 Conclusion -- References -- Chapter 5 Mitigating the Threat of Multi-Factor Authentication (MFA) Bypass Through Man-in-the-Middle Attacks Using EvilGinx2 -- 5.1 Introduction -- 5.1.1 Background and Significance of MFA in Enhancing Account Security -- 5.1.2 Overview of the Research Topic and the Use of EvilGinx2 for MFA Bypass -- 5.1.3 Research Objectives and Research Questions -- 5.2 Literature Review -- 5.2.1 Overview of MFA and its Effectiveness in Preventing Unauthorized Access -- 5.2.2 Previous Research on MFA Vulnerabilities and Bypass Techniques -- 5.2.3 Case Studies -- 5.3 Methodology -- 5.3.1 Description of Experimental Setup and Environment -- 5.3.2 Demonstration of EvilGinx2's Functionality and Operation -- 5.4 Results and Discussion -- 5.4.1 Evaluation of EvilGinx2's Ability to Bypass MFA Protections -- 5.4.2 Analysis of Captured Authentication Data, Including Usernames, Passwords, and Cookies -- 5.4.3 Discussion of the Effectiveness of the MFA Bypass Technique. 5.4.4 Identification of Potential Vulnerabilities and Areas of Improvement -- 5.5 Conclusion -- 5.5.1 Summary of the Research Objectives and Main Findings -- 5.5.2 Contribution to the Field of Cybersecurity and MFA Protection -- 5.5.3 Implications for Organizations and Recommendations for Future Research -- References -- Chapter 6 Implementation of Rule-Based DDoS Solution in Software-Defined Network -- 6.1 Introduction -- 6.2 Background Study -- 6.2.1 Software-Defined Network (SDN) -- 6.2.2 Software-Defined Architecture -- 6.2.2.1 Application Layer -- 6.2.2.2 Control Layer -- 6.2.2.3 Data Layer -- 6.2.3 OpenFlow Protocol -- 6.2.4 Flow Table -- 6.2.5 Advantages of SDN -- 6.2.6 Vulnerabilities of SDN and OpenFlow -- 6.2.6.1 SYN Flag DDoS Attacks in SDN -- 6.2.6.2 Three-Way Handshake in TCP Protocol -- 6.3 Critical Literature Review -- 6.3.1 Machine Learning-Based Mitigation -- 6.3.1.1 Limitations of Machine Learning-Based Approach -- 6.3.2 Statistical-Based Mitigation -- 6.3.2.1 Limitations of Statistical-Based Approach -- 6.3.3 Rule-Based Mitigation -- 6.3.3.1 Limitations of Rule-Based Approach -- 6.4 Methodologies -- 6.4.1 System Configuration -- 6.4.2 Static Threshold Rule-Based Approach -- 6.4.3 Testing -- 6.5 Results and Discussion -- 6.5.1 Reflection and Future Scope -- 6.6 Conclusion -- References -- Chapter 7 Securing Network Data with a Novel Encryption Scheme -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Proposed System and Methodology -- 7.3.1 Advanced Encryption Standards (AES) -- 7.3.2 RSA Public-Key Encryption -- 7.4 Results and Discussion -- 7.4.1 Result Analysis -- 7.4.1.1 CRC Generation Time -- 7.4.1.2 Encryption Time -- 7.4.1.3 Decryption Time -- 7.4.1.4 CRC Checker Time -- 7.4.1.5 CRC Generation Memory -- 7.4.1.6 Encryption Memory -- 7.4.1.7 CRC Checker Memory -- 7.4.1.8 Decryption Memory -- 7.4.1.9 Error Detection Capability. 7.5 Conclusion and Future Works -- References -- Chapter 8 A Robust Authentication Technique for Client-Server Secure Login -- 8.1 Introduction -- 8.2 Preliminary Concept -- 8.2.1 Cryptography -- 8.2.2 Symmetric Key Algorithms -- 8.2.3 Asymmetric Key Algorithms -- 8.2.4 Hash Function -- 8.2.5 Key Exchange -- 8.3 Related Work -- 8.3.1 Transport Layer Security (TLS) Protocol -- 8.3.2 Kerberos -- 8.3.3 Secure Remote Password (SRP) Protocol -- 8.3.4 OAuth -- 8.3.5 Mutual Authentication -- 8.4 Proposed Technique -- 8.4.1 Key-Generation Phase -- 8.4.2 Registration Phase -- 8.4.3 Login Phase -- 8.4.4 Principles of the Algorithm -- 8.5 Implementation -- 8.6 Discussion -- 8.6.1 Security Analysis -- 8.6.2 Security Features and Performance Comparison -- 8.7 Conclusion -- References -- Chapter 9 Application of a Web-Based Food Ordering Platform to Minimize Food Wastage and Prevent Theft -- 9.1 Introduction -- 9.1.1 Background -- 9.1.2 The Food Wastage Issues in Cafeterias -- 9.1.3 Overcrowding and Long Queues in Cafeteria -- 9.1.4 Theft and Delivery Assurance -- 9.1.5 Scalability of Cafeteria Operations -- 9.1.6 Lack of Use of Technology in School Cafeteria -- 9.1.7 Problems with Traditional Software Architecture -- 9.2 Literature Review -- 9.2.1 The Current Food Wastage Problem in Cafeterias -- 9.2.2 Identifying Gaps to Overcome Overcrowding Issues in Cafeteria -- 9.2.2.1 Similar Food Ordering Solutions -- 9.2.3 Research Gaps -- 9.3 Methodology -- 9.3.1 Introduction -- 9.3.2 Feature Requirements -- 9.3.3 System Architecture Requirements -- 9.3.4 Technical Requirements -- 9.3.5 Infrastructure Design -- 9.3.6 Software Architecture -- 9.3.7 IoT Integrations -- 9.3.8 Database Schema -- 9.4 Discussion -- 9.4.1 Advantages -- 9.4.2 Limitations -- 9.4.3 Comparison to Other Implementations -- 9.5 Conclusion -- References -- Part 2: IT in the Textile Industry. Chapter 10 Research Design Machine Maintenance Management Software Module for Garment Industry -- 10.1 Introduction -- 10.2 Building a Maintenance Process for Garment Industry Machine -- 10.2.1 Maintenance Process for Machinery -- 10.2.2 Information in the Maintenance Management Machine Records -- 10.3 Designing a "Machine Maintenance Management" Software Module -- 10.3.1 Database Design -- 10.3.2 Designing a "Machine Maintenance Management" Software Module -- 10.4 Conclusion -- References -- Part 3: Adoption of ICT for Digitalization, Artificial Intelligence, and Machine Learning -- Chapter 11 Performance Comparison of Prediction of a Hydraulic Jump Depth in a Channel Using Various Machine Learning Models -- Nomenclature -- 11.1 Introduction -- 11.2 Related Works -- 11.3 Materials and Methods -- 11.3.1 Equation of the Hydraulic Jump -- 11.3.2 Data Used in the Study -- 11.4 Machine Learning Models -- 11.4.1 Features of Machine Learning Models -- 11.4.2 Support Vector Machine (SVM) -- 11.4.3 Decision Tree (DT) -- 11.4.4 Random Forest (RF) -- 11.4.5 Artificial Neural Network (ANN) -- 11.5 Results and Discussion -- 11.6 Conclusions -- References -- Chapter 12 Creating a Video from Facial Image Using Conditional Generative Adversarial Network -- 12.1 Introduction -- 12.2 Related Works -- 12.3 Methodology -- 12.3.1 The Proposed Model -- 12.3.2 Conditional Generative Adversarial Network (cGAN) -- 12.3.3 Hidden Affine Transformation -- 12.4 Experiments -- 12.4.1 Dataset -- 12.4.2 Dlib -- 12.4.3 Evaluation -- 12.4.4 Result -- 12.5 Conclusion -- References -- Chapter 13 Deep Learning Framework for Detecting, Classifying, and Recognizing Invoice Metadata -- 13.1 Introduction -- 13.2 Related Works -- 13.3 Invoice Data Analysis -- 13.4 Proposed Method -- 13.5 Experiments -- 13.6 Conclusion and Perspectives -- References. Chapter 14 Artificial Neural Network-Based Approach for Molecular Bitter Prediction. |
| Record Nr. | UNINA-9911019977503321 |
Bijalwan Anchit
|
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data structure and algorithms using C++ : a practical implementation / / edited by Sachi Nandan Mohanty and Pabitra Kumar Tripathy
| Data structure and algorithms using C++ : a practical implementation / / edited by Sachi Nandan Mohanty and Pabitra Kumar Tripathy |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , 2021 |
| Descrizione fisica | 1 online resource (416 pages) |
| Disciplina | 005.73 |
| Soggetto topico | Data structures (Computer science) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-75204-3
1-119-75205-1 1-119-75203-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910555098603321 |
| Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data structure and algorithms using C++ : a practical implementation / / edited by Sachi Nandan Mohanty and Pabitra Kumar Tripathy
| Data structure and algorithms using C++ : a practical implementation / / edited by Sachi Nandan Mohanty and Pabitra Kumar Tripathy |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , 2021 |
| Descrizione fisica | 1 online resource (416 pages) |
| Disciplina | 005.73 |
| Soggetto topico | Data structures (Computer science) |
| ISBN |
1-119-75204-3
1-119-75205-1 1-119-75203-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910677236203321 |
| Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data structure and algorithms using C++ : a practical implementation / / edited by Sachi Nandan Mohanty and Pabitra Kumar Tripathy
| Data structure and algorithms using C++ : a practical implementation / / edited by Sachi Nandan Mohanty and Pabitra Kumar Tripathy |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , 2021 |
| Descrizione fisica | 1 online resource (416 pages) |
| Disciplina | 005.73 |
| Soggetto topico | Data structures (Computer science) |
| ISBN |
1-119-75204-3
1-119-75205-1 1-119-75203-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910823934703321 |
| Hoboken, NJ : , : Wiley : , : Scrivener Publishing, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Decision making and problem solving : a practical guide for applied research / / edited by Sachi Nandan Mohanty
| Decision making and problem solving : a practical guide for applied research / / edited by Sachi Nandan Mohanty |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (xiii, 111 pages) : illustrations |
| Disciplina | 150 |
| Soggetto topico |
Psychology
Presa de decisions Resolució de problemes |
| Soggetto genere / forma |
Llibres en Braille
Llibres electrònics |
| ISBN | 3-030-66869-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483332503321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Demystifying AI and ML for Cyber–Threat Intelligence / / edited by Ming Yang, Sachi Nandan Mohanty, Suneeta Satpathy, Shu Hu
| Demystifying AI and ML for Cyber–Threat Intelligence / / edited by Ming Yang, Sachi Nandan Mohanty, Suneeta Satpathy, Shu Hu |
| Autore | Yang Ming |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (712 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
MohantySachi Nandan
SatpathySuneeta HuShu |
| Collana | Information Systems Engineering and Management |
| Soggetto topico |
Computational intelligence
Data protection Engineering - Data processing Artificial intelligence Computational Intelligence Data and Information Security Data Engineering Artificial Intelligence |
| ISBN | 3-031-90723-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Comprehensive Review on the Detection Capabilities of IDS using Deep Learning Techniques -- Next-Generation Intrusion Detection Framework with Active Learning-Driven Neural Networks for DDoS Defense -- Ensemble Learning-based Intrusion Detection System for RPL-based IoT Networks -- Advancing Detection of Man-in-the-Middle Attacks through Possibilistic C-Means Clustering -- CNN-Based IDS for Internet of Vehicles Using Transfer Learning -- Real-Time Network Intrusion Detection System using Machine Learning -- OpIDS-DL : OPTIMIZING INTRUSION DETECTION IN IoT NETWORKS: A DEEP LEARNING APPROACH WITH REGULARIZATION AND DROPOUT FOR ENHANCED CYBERSECURITY -- ML-Powered Sensitive Data Loss Prevention Firewall for Generative AI Applications -- Enhancing Data Integrity: Unveiling the Potential of Reversible Logic for Error Detection and Correction -- Enhancing Cyber security through Reversible Logic -- Beyond Passwords: Enhancing Security with Continuous Behavioral Biometrics and Passive Authentication. |
| Record Nr. | UNINA-9911021155303321 |
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Yang Ming
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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