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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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Yang Ming  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Explainable IoT Applications: A Demystification / / edited by Sachi Nandan Mohanty, Suneeta Satpathy, Xiaochun Cheng, Subhendu Kumar Pani
Explainable IoT Applications: A Demystification / / edited by Sachi Nandan Mohanty, Suneeta Satpathy, Xiaochun Cheng, Subhendu Kumar Pani
Autore Mohanty Sachi Nandan
Edizione [1st ed. 2025.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
Descrizione fisica 1 online resource (517 pages)
Disciplina 006.3
Altri autori (Persone) SatpathySuneeta
ChengXiaochun
PaniSubhendu Kumar
Collana Information Systems Engineering and Management
Soggetto topico Computational intelligence
Engineering - Data processing
Artificial intelligence
Computational Intelligence
Data Engineering
Artificial Intelligence
ISBN 9783031748851
3031748859
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Essential Uses of IoT and Machine Learning -- IoT Pro-Interventions: Transforming Industries and Enhancing Quality of Life -- A Comprehensive Review of Machine Learning Approaches in IoT and Cyber Security for Information Systems Analysis -- Application of Machine learning in the Internet of Things -- Empowering Industries with IoT and Machine Learning Innovations -- A Framework for Sustainable Smart Healthcare Systems in Smart Cities -- Cloud Computing Applications in Digital Health: Challenges related to Privacy and Safety -- An IoT-Based Blockchain-Enabled Secure Storage for Healthcare Systems -- Block-Chain Technology in Smart Telemedicine using IOT -- Securing the Future of IoT-Based Smart Healthcare: Challenges, Innovations, and Best Practice -- Smart City: Challenges & Opportunities Detection and Identification of Autonomous Vehicles Using Sensor Synthesis -- AN IOT BASED REAL TIME TRAFFIC MONITORING SYSTEM -- Internet of Things enabled Technological devices empowering expertise in improve Smart City operations -- Enhancing Smart City Retail: An Innovative IoT Driven Smart Billing-Enabled Shopping Cart -- Smart City: Challenges and Issues -- IoT based Real-Time Ecological Monitoring System Deploying an Arduino Board and Cloud Computing -- IoT Based Monitoring Of Waste Management And Air Pollutants -- IOT based smart dustbin design and implementation for monitoring under uncertain environments -- Smart garbage monitoring system using IOT for commercial purpose -- IoT Based Smart Home Systems -- A Survey on Various Secure Access Control and Authentication in a Block Chain -Enable Cloud IoT -- Uncovering the Truth: A Machine Learning Approach to Detect Fake Product Reviews and analyze Sentiment -- Real Time Fall Detection monitoring on elderly using IoT and Deep Learning -- CNN’s augmented with IoT for Traffic Optimization and Signal Regulation -- CVLSTMLW-CNN:A IoT-Enabled Hybrid CNN model for Heart Disease Prediction -- Advancements in Security Technologies for Smart Cities: A Comprehensive Overview -- A Deep Learning Framework based on Convolutional Neural Network for Automatic Detection of Cyberattacks in IoT Use Cases -- Digital Attack Identification for the Internet of Things Using Machine Learning -- IoT Applications and Cyber Threats: Mitigation Strategies for a Secure Future -- Internet of Things and OpenCV-Based Smart Posture Recognition Chair -- Security concerns in low power networks for Internet of Things (IoT) -- Comprehensive Review of Security Challenges and issues in Wireless Sensor Networks Integrated with IoT.
Record Nr. UNINA-9910983327603321
Mohanty Sachi Nandan  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Internet of Things and Its Applications
Internet of Things and Its Applications
Autore Nandan Mohanty Sachi
Pubbl/distr/stampa Cham : , : Springer International Publishing AG, , 2022
Descrizione fisica 1 online resource (562 pages)
Altri autori (Persone) ChatterjeeJyotir Moy
SatpathySuneeta
Collana EAI/Springer Innovations in Communication and Computing Ser.
Soggetto genere / forma Electronic books.
ISBN 9783030775285
9783030775278
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgment -- Contents -- About the Authors -- Part I: IoT -Foundations, Architectures & -- Smart Services -- Internet of Things: Basic Concepts and Decorum of Smart Services -- 1 Introduction -- 1.1 Level of IoT -- 1.2 Discussion of Major Components for IoT-Based Smart Farming -- 2 IoT's Role in Application -- 2.1 WSNs -- 2.2 Characteristics of the Wireless Sensor Network -- 2.3 Wireless Architecture -- 2.4 Network Topology Construction Phase with Efficient Processing -- 2.5 IoT Agricultural Network Architecture -- 3 Cloud and Fog Infrastructure for Data Security -- 4 COVID Handling Using IoT -- 5 Conclusion -- References -- IoT Framework, Architecture Services, Platforms, and Reference Models -- 1 Introduction -- 1.1 Definitions -- 1.2 IoT Technologies -- 1.2.1 Radio-Frequency Identification (RFID) -- 1.2.2 Internet Protocol (IP) -- 1.2.3 Electronic Product Code (EPC) -- 1.2.4 Barcode -- 1.2.5 Wireless Fidelity -- 1.2.6 Bluetooth -- 1.2.7 Zigbee -- 1.2.8 Near Field Communication (NFC) -- 1.2.9 Wireless Sensor Networks (WSN) -- 1.3 IoT Framework -- 1.4 IoT Architecture -- 1.4.1 Four Stages of IoT Architecture -- 1.4.2 Basic IoT Architecture -- 1.4.3 Three-Layered Architecture -- 1.4.4 Four-Layered Architecture -- 1.4.5 Five-Layered Architecture -- 1.4.6 European FP7 Research Project -- 1.4.7 ITU Architecture and IoT Forum Architecture -- 1.4.8 Qian Xiao Cong, Zhang Jidong Architecture -- 1.4.9 Cloud-Based Architectures -- 1.5 IoT Platform -- 1.5.1 Google Cloud Platform -- 1.5.2 IBM BlueMix -- 1.5.3 ThingWorx -- 1.5.4 Microsoft Azure Cloud -- 1.5.5 ThingSpeak -- 1.5.6 Digital Service Cloud -- 1.5.7 Zetta -- 1.5.8 Yaler -- 1.5.9 Amazon Web Services -- 1.5.10 Seven Levels of IoT Reference Model -- 1.6 Brief Introduction to IoT Analytics -- 1.7 Challenges of IoT -- 1.8 Conclusion -- References.
Part II: Smart Healthcare & -- IoT -- A Check on WHO Protocol Implementation for COVID-19 Using IoT -- 1 Introduction -- 2 Literature Survey -- 2.1 Literature Survey Conclusion -- 3 Dataset -- 4 Proposed System -- 4.1 Designed Convolutional Neural Network -- 4.2 Raspberry Pi's Setup -- 4.2.1 Pi Camera -- 4.2.2 MLX90614 Non-contact Temperature Sensor -- 5 Implementation -- 5.1 CNN Algorithm -- 6 Results -- 7 Conclusion -- References -- Design and Implementation of an Internet of Things (IoT) Architecture for the Acquisition of Relevant Variables in the Study of Failures in Medical Equipment: A Case Study -- 1 Introduction -- 2 Related Works -- 3 Proposed Work -- 3.1 System Architecture and Variables Measured -- 3.1.1 Sensing Layer -- 3.1.2 Network Layer -- 3.1.3 The Service Layer -- 4 Results -- 4.1 System Architecture and Variables Measured -- 5 Discussion -- 6 Conclusions -- 7 Future Work -- References -- A Novel IoT-Based Solution for Respiratory Flow Diagnosis -- 1 Introduction -- 2 Related Works -- 3 Overview of Acquisition and Control Modules -- 3.1 Proposed System to Measure Exhaled Airflow Rate -- 4 Design of Experiment -- 5 Results and Discussion -- 6 Conclusion -- References -- Deep Learning Application in Classification of Brain Metastases: Sensor Usage in Medical Diagnosis for Next Gen Healthcare -- 1 Introduction -- 1.1 Brain Tumor -- 1.2 Big Data Analytics in Health Informatics -- 1.3 Machine Learning in Healthcare -- 1.4 Sensors for Internet of Things -- 1.5 Let Us Look at Some Stats to See the Progress of IOT in Healthcare -- 1.6 Challenges and Critical Issues of IOT in Healthcare -- 1.7 Machine Learning and Artificial Intelligence (AI) for Health Informatics -- 1.8 Health Sensor Data Management -- 1.9 Multimodal Data Fusion for Healthcare.
1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT -- 1.11 Role of Technology in Addressing the Problem of Integration of Healthcare System -- 2 Literature Survey -- 3 System Design and Methodology -- 3.1 System Design -- 3.2 CNN Architecture -- 3.3 Block Diagram -- 3.4 Algorithm(s) -- 4 Our Experimental Results, Interpretation, and Discussion -- 4.1 Experimental Setup -- 4.2 Implementation Details -- 4.3 Snapshots of Interfaces -- 5 Novelty in Our Work -- 6 Future Scope, Possible Applications, and Limitations -- 7 Recommendations and Consideration -- 8 Conclusions -- 9 Performance Evaluations -- 9.1 Comparison with Other Algorithms -- Annex -- Key Terms and Definitions -- B. Additional Readings -- References -- Implementation of Smart Control of Wheelchair for a Disabled Person -- 1 Introduction -- 2 Related Work -- 3 System Design -- 4 Results and Discussion -- 5 Conclusion -- References -- Application of the Internet of Things (IoT) in Biomedical Engineering: Present Scenario and Challenges -- 1 Introduction -- 2 Applications to Health Care -- 2.1 Health Monitoring System -- 2.2 Remote Steady ECG Checking -- 2.3 Telemedicine Innovation -- 2.4 RFID Applications to Assist the Elderly to Live Independently -- 2.5 Portable Medicine -- 2.6 Utilizations of RFID Wristbands -- 2.7 GPS Positioning Applications for Patients with Heart Disease -- 2.8 Prediction of Protein Structure -- 3 Specialized Problems Facing Medical IoT -- 3.1 Node Versatility and Dynamic Large-Scale System: The Board in Enormous Scale Systems -- 3.2 Information Completeness and Data Compression -- 3.3 Information Security -- 3.4 Duplicate Medicine Detection -- 4 Conclusion -- References.
Risk Stratification for Subjects Suffering from Lung Carcinoma: Healthcare 4.0 Approach with Medical Diagnosis Using Computational Intelligence -- 1 Introduction -- 1.1 Motivation to the Study -- 1.1.1 Problem Statements -- 1.1.2 Authors' Contributions -- 1.1.3 Research Manuscript Organization -- 1.2 Definitions -- 1.2.1 Computer-Aided Diagnosis System (CADe or CADx) -- 1.2.2 Sensors for the Internet of Things -- 1.2.3 Wireless and Wearable Sensors for Health Informatics -- 1.2.4 Remote Human's Health and Activity Monitoring -- 1.2.5 Decision-Making Systems for Sensor Data -- 1.2.6 Artificial Intelligence (AI) and Machine Learning for Health Informatics -- 1.2.7 Health Sensor Data Management -- 1.2.8 Multimodal Data Fusion for Healthcare -- 1.2.9 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT -- 2 Literature Review -- 3 Proposed Systems -- 3.1 Framework or Architecture of the Work -- 3.2 Model Steps and Parameters -- 3.3 Discussions -- 4 Experimental Results and Analysis -- 4.1 Tissue Characterization and Risk Stratification -- 4.2 Samples of Cancer Data and Analysis -- 5 Novelties -- 6 Future Scopes, Limitations, and Possible Applications -- 7 Recommendations and Considerations -- 8 Conclusions -- Annex -- Key Terms and Definitions -- Additional Readings (Addendum) -- Data Set -- Snapshots of the Implementation -- References -- The Fusion of IOT and Wireless Body Area Network -- 1 Introduction -- 1.1 WBAN System Architecture -- 1.2 Applications of WBANs -- 1.2.1 Cardiovascular Application -- 1.2.2 Cancer Detection -- 1.2.3 Blood Glucose Monitoring -- 1.2.4 Stress Monitoring -- 1.2.5 Artificial Retina -- 1.2.6 General Health Monitoring -- 1.2.7 Non-medical Applications -- 2 Review of Existing Works -- 3 Fusion of IoT with WBAN -- 3.1 Starting Stage -- 3.2 Cluster Evolution.
3.3 Sensed Information Stage -- 3.4 Choice of Forwarder Stage -- 3.5 Consumed Energy as well as Routing Stage -- 3.6 Model of Network -- 3.6.1 Model of Energy -- 3.6.2 Model of Path Loss -- 3.6.3 Particle Swarm Optimization Algorithm -- Initialization -- Fitness Function's Evaluation -- Hunting -- Particles' Upgraded Velocity as well as Allocation -- Local Best as well as Global Best Upgrading -- 3.7 Optimized Approaches -- 3.7.1 System Model -- 3.7.2 Starting Stage -- Transmission of Data Stage -- 4 MC-MAC Strategy for Interference Reduction Inside WBANs -- 4.1 WBANs and Healthcare -- 4.2 Protocols of Multi-channel -- 5 Conclusion -- References -- Part III: Smart Education & -- IoT -- Paradigms of Smart Education with IoT Approach -- 1 Introduction -- 2 Meaning of "Smart" in Smart Education -- 2.1 Smart Campus -- 2.2 Smart Learner -- 2.3 Handheld Devices -- 2.4 Smart Tracking and Monitoring System -- 2.5 Smart Learning Environment -- 2.6 Smart Pedagogies -- 2.7 Increased Security -- 2.8 Smart Learning for Disable Students -- 3 IoT in Smart Education -- 4 Conclusion -- References -- Automated Electric Power Saving System in University Classrooms Using Internet of Things -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Algorithm Used for Implementing the Model -- 4 Results and Effectiveness of Proposed Methodology -- 5 Advantages, Disadvantages, and Applications of Using Proposed Methodology -- 6 Conclusion and Future Directions -- 6.1 Conclusion -- 6.2 Future Directions -- References -- Part IV: Smart Banking & -- IoT -- Smart Banking in Financial Transactions of Migrants: A Study on the In-Migrants of the Gajapati District of Odisha -- 1 Introduction -- 2 Review of Literature -- 3 Objectives -- 4 Methodology.
5 Availability and Accessibility of Smart Banking Facilities to Migrant Workers Staying in the Gajapati District of Odisha.
Record Nr. UNINA-9910510574303321
Nandan Mohanty Sachi  
Cham : , : Springer International Publishing AG, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Internet of things and its applications / / Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Suneeta Satpathy, editors
Internet of things and its applications / / Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Suneeta Satpathy, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (562 pages)
Disciplina 004.678
Collana EAI/Springer innovations in communication and computing
Soggetto topico Internet of things
Internet in medicine
Internet in education
ISBN 3-030-77528-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgment -- Contents -- About the Authors -- Part I: IoT -Foundations, Architectures & -- Smart Services -- Internet of Things: Basic Concepts and Decorum of Smart Services -- 1 Introduction -- 1.1 Level of IoT -- 1.2 Discussion of Major Components for IoT-Based Smart Farming -- 2 IoT's Role in Application -- 2.1 WSNs -- 2.2 Characteristics of the Wireless Sensor Network -- 2.3 Wireless Architecture -- 2.4 Network Topology Construction Phase with Efficient Processing -- 2.5 IoT Agricultural Network Architecture -- 3 Cloud and Fog Infrastructure for Data Security -- 4 COVID Handling Using IoT -- 5 Conclusion -- References -- IoT Framework, Architecture Services, Platforms, and Reference Models -- 1 Introduction -- 1.1 Definitions -- 1.2 IoT Technologies -- 1.2.1 Radio-Frequency Identification (RFID) -- 1.2.2 Internet Protocol (IP) -- 1.2.3 Electronic Product Code (EPC) -- 1.2.4 Barcode -- 1.2.5 Wireless Fidelity -- 1.2.6 Bluetooth -- 1.2.7 Zigbee -- 1.2.8 Near Field Communication (NFC) -- 1.2.9 Wireless Sensor Networks (WSN) -- 1.3 IoT Framework -- 1.4 IoT Architecture -- 1.4.1 Four Stages of IoT Architecture -- 1.4.2 Basic IoT Architecture -- 1.4.3 Three-Layered Architecture -- 1.4.4 Four-Layered Architecture -- 1.4.5 Five-Layered Architecture -- 1.4.6 European FP7 Research Project -- 1.4.7 ITU Architecture and IoT Forum Architecture -- 1.4.8 Qian Xiao Cong, Zhang Jidong Architecture -- 1.4.9 Cloud-Based Architectures -- 1.5 IoT Platform -- 1.5.1 Google Cloud Platform -- 1.5.2 IBM BlueMix -- 1.5.3 ThingWorx -- 1.5.4 Microsoft Azure Cloud -- 1.5.5 ThingSpeak -- 1.5.6 Digital Service Cloud -- 1.5.7 Zetta -- 1.5.8 Yaler -- 1.5.9 Amazon Web Services -- 1.5.10 Seven Levels of IoT Reference Model -- 1.6 Brief Introduction to IoT Analytics -- 1.7 Challenges of IoT -- 1.8 Conclusion -- References.
Part II: Smart Healthcare & -- IoT -- A Check on WHO Protocol Implementation for COVID-19 Using IoT -- 1 Introduction -- 2 Literature Survey -- 2.1 Literature Survey Conclusion -- 3 Dataset -- 4 Proposed System -- 4.1 Designed Convolutional Neural Network -- 4.2 Raspberry Pi's Setup -- 4.2.1 Pi Camera -- 4.2.2 MLX90614 Non-contact Temperature Sensor -- 5 Implementation -- 5.1 CNN Algorithm -- 6 Results -- 7 Conclusion -- References -- Design and Implementation of an Internet of Things (IoT) Architecture for the Acquisition of Relevant Variables in the Study of Failures in Medical Equipment: A Case Study -- 1 Introduction -- 2 Related Works -- 3 Proposed Work -- 3.1 System Architecture and Variables Measured -- 3.1.1 Sensing Layer -- 3.1.2 Network Layer -- 3.1.3 The Service Layer -- 4 Results -- 4.1 System Architecture and Variables Measured -- 5 Discussion -- 6 Conclusions -- 7 Future Work -- References -- A Novel IoT-Based Solution for Respiratory Flow Diagnosis -- 1 Introduction -- 2 Related Works -- 3 Overview of Acquisition and Control Modules -- 3.1 Proposed System to Measure Exhaled Airflow Rate -- 4 Design of Experiment -- 5 Results and Discussion -- 6 Conclusion -- References -- Deep Learning Application in Classification of Brain Metastases: Sensor Usage in Medical Diagnosis for Next Gen Healthcare -- 1 Introduction -- 1.1 Brain Tumor -- 1.2 Big Data Analytics in Health Informatics -- 1.3 Machine Learning in Healthcare -- 1.4 Sensors for Internet of Things -- 1.5 Let Us Look at Some Stats to See the Progress of IOT in Healthcare -- 1.6 Challenges and Critical Issues of IOT in Healthcare -- 1.7 Machine Learning and Artificial Intelligence (AI) for Health Informatics -- 1.8 Health Sensor Data Management -- 1.9 Multimodal Data Fusion for Healthcare.
1.10 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT -- 1.11 Role of Technology in Addressing the Problem of Integration of Healthcare System -- 2 Literature Survey -- 3 System Design and Methodology -- 3.1 System Design -- 3.2 CNN Architecture -- 3.3 Block Diagram -- 3.4 Algorithm(s) -- 4 Our Experimental Results, Interpretation, and Discussion -- 4.1 Experimental Setup -- 4.2 Implementation Details -- 4.3 Snapshots of Interfaces -- 5 Novelty in Our Work -- 6 Future Scope, Possible Applications, and Limitations -- 7 Recommendations and Consideration -- 8 Conclusions -- 9 Performance Evaluations -- 9.1 Comparison with Other Algorithms -- Annex -- Key Terms and Definitions -- B. Additional Readings -- References -- Implementation of Smart Control of Wheelchair for a Disabled Person -- 1 Introduction -- 2 Related Work -- 3 System Design -- 4 Results and Discussion -- 5 Conclusion -- References -- Application of the Internet of Things (IoT) in Biomedical Engineering: Present Scenario and Challenges -- 1 Introduction -- 2 Applications to Health Care -- 2.1 Health Monitoring System -- 2.2 Remote Steady ECG Checking -- 2.3 Telemedicine Innovation -- 2.4 RFID Applications to Assist the Elderly to Live Independently -- 2.5 Portable Medicine -- 2.6 Utilizations of RFID Wristbands -- 2.7 GPS Positioning Applications for Patients with Heart Disease -- 2.8 Prediction of Protein Structure -- 3 Specialized Problems Facing Medical IoT -- 3.1 Node Versatility and Dynamic Large-Scale System: The Board in Enormous Scale Systems -- 3.2 Information Completeness and Data Compression -- 3.3 Information Security -- 3.4 Duplicate Medicine Detection -- 4 Conclusion -- References.
Risk Stratification for Subjects Suffering from Lung Carcinoma: Healthcare 4.0 Approach with Medical Diagnosis Using Computational Intelligence -- 1 Introduction -- 1.1 Motivation to the Study -- 1.1.1 Problem Statements -- 1.1.2 Authors' Contributions -- 1.1.3 Research Manuscript Organization -- 1.2 Definitions -- 1.2.1 Computer-Aided Diagnosis System (CADe or CADx) -- 1.2.2 Sensors for the Internet of Things -- 1.2.3 Wireless and Wearable Sensors for Health Informatics -- 1.2.4 Remote Human's Health and Activity Monitoring -- 1.2.5 Decision-Making Systems for Sensor Data -- 1.2.6 Artificial Intelligence (AI) and Machine Learning for Health Informatics -- 1.2.7 Health Sensor Data Management -- 1.2.8 Multimodal Data Fusion for Healthcare -- 1.2.9 Heterogeneous Data Fusion and Context-Aware Systems: A Context-Aware Data Fusion Approach for Health-IoT -- 2 Literature Review -- 3 Proposed Systems -- 3.1 Framework or Architecture of the Work -- 3.2 Model Steps and Parameters -- 3.3 Discussions -- 4 Experimental Results and Analysis -- 4.1 Tissue Characterization and Risk Stratification -- 4.2 Samples of Cancer Data and Analysis -- 5 Novelties -- 6 Future Scopes, Limitations, and Possible Applications -- 7 Recommendations and Considerations -- 8 Conclusions -- Annex -- Key Terms and Definitions -- Additional Readings (Addendum) -- Data Set -- Snapshots of the Implementation -- References -- The Fusion of IOT and Wireless Body Area Network -- 1 Introduction -- 1.1 WBAN System Architecture -- 1.2 Applications of WBANs -- 1.2.1 Cardiovascular Application -- 1.2.2 Cancer Detection -- 1.2.3 Blood Glucose Monitoring -- 1.2.4 Stress Monitoring -- 1.2.5 Artificial Retina -- 1.2.6 General Health Monitoring -- 1.2.7 Non-medical Applications -- 2 Review of Existing Works -- 3 Fusion of IoT with WBAN -- 3.1 Starting Stage -- 3.2 Cluster Evolution.
3.3 Sensed Information Stage -- 3.4 Choice of Forwarder Stage -- 3.5 Consumed Energy as well as Routing Stage -- 3.6 Model of Network -- 3.6.1 Model of Energy -- 3.6.2 Model of Path Loss -- 3.6.3 Particle Swarm Optimization Algorithm -- Initialization -- Fitness Function's Evaluation -- Hunting -- Particles' Upgraded Velocity as well as Allocation -- Local Best as well as Global Best Upgrading -- 3.7 Optimized Approaches -- 3.7.1 System Model -- 3.7.2 Starting Stage -- Transmission of Data Stage -- 4 MC-MAC Strategy for Interference Reduction Inside WBANs -- 4.1 WBANs and Healthcare -- 4.2 Protocols of Multi-channel -- 5 Conclusion -- References -- Part III: Smart Education & -- IoT -- Paradigms of Smart Education with IoT Approach -- 1 Introduction -- 2 Meaning of "Smart" in Smart Education -- 2.1 Smart Campus -- 2.2 Smart Learner -- 2.3 Handheld Devices -- 2.4 Smart Tracking and Monitoring System -- 2.5 Smart Learning Environment -- 2.6 Smart Pedagogies -- 2.7 Increased Security -- 2.8 Smart Learning for Disable Students -- 3 IoT in Smart Education -- 4 Conclusion -- References -- Automated Electric Power Saving System in University Classrooms Using Internet of Things -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Algorithm Used for Implementing the Model -- 4 Results and Effectiveness of Proposed Methodology -- 5 Advantages, Disadvantages, and Applications of Using Proposed Methodology -- 6 Conclusion and Future Directions -- 6.1 Conclusion -- 6.2 Future Directions -- References -- Part IV: Smart Banking & -- IoT -- Smart Banking in Financial Transactions of Migrants: A Study on the In-Migrants of the Gajapati District of Odisha -- 1 Introduction -- 2 Review of Literature -- 3 Objectives -- 4 Methodology.
5 Availability and Accessibility of Smart Banking Facilities to Migrant Workers Staying in the Gajapati District of Odisha.
Record Nr. UNINA-9910522931903321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Protecting and Mitigating Against Cyber Threats : Deploying Artificial Intelligence and Machine Learning
Protecting and Mitigating Against Cyber Threats : Deploying Artificial Intelligence and Machine Learning
Autore Mohanty Sachi Nandan
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2025
Descrizione fisica 1 online resource (561 pages)
Disciplina 005.8
Altri autori (Persone) SatpathySuneeta
YangMing
ValiD. Khasim
Soggetto topico Computer security
ISBN 1-394-30521-4
1-394-30519-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: Foundations of AI & -- ML in Security -- Chapter 1 Foundations of AI and ML in Security -- Abbreviations -- 1.1 Introduction -- 1.1.1 The Convergence of AI and ML in Security -- 1.2 Understanding Security Attacks -- 1.2.1 Types of Attacks and Vulnerability -- 1.2.2 How Attacks Exploit Vulnerabilities -- 1.2.3 Real-World Examples of AI and ML for Security -- 1.3 Evolution of Information, Cyber Issues/Threats Attacks -- 1.3.1 Cyber Security Threats -- 1.3.2 The Most Prevalent Security Attacks -- 1.4 Machine Learning for Security and Vulnerability -- 1.4.1 Data Collection and Preprocessing -- 1.4.2 Feature Engineering for Security Attack Detection -- 1.5 Challenges and Future Directions -- 1.6 Summary -- References -- Chapter 2 Application of AI and ML in Threat Detection -- 2.1 Introduction -- 2.2 Foundation of AI and ML in Security -- 2.2.1 Definition and Concepts -- 2.2.2 Types of Artificial Intelligence -- 2.2.3 Algorithms and Models in Machine Learning -- 2.3 AI and ML in Applications in Threat Detection -- 2.3.1 Next-Generation Endpoint Protection -- 2.3.2 Endpoint Detection and Response (EDR) -- 2.4 AI/ML Based Network Intrusion Detection Systems (NIDS) -- 2.5 Threat Intelligence and Predictive Analytics -- 2.6 Challenges and Considerations -- 2.7 Integration and Interoperability -- 2.8 Future Directions -- 2.9 Conclusion -- References -- Chapter 3 Artificial Intelligence and Machine Learning Applications in Threat Detection -- 3.1 Introduction -- 3.2 Foundations of Threat Detection -- 3.2.1 Traditional Threat Detection Methods -- 3.2.2 The Need for Advanced Technologies -- 3.3 Overview of AI and ML -- 3.3.1 Understanding Artificial Intelligence -- 3.3.2 Machine Learning Fundamentals -- 3.4 AI and ML Techniques for Threat Detection.
3.4.1 Supervised Learning and Unsupervised Learning -- 3.4.2 Deep Learning -- 3.5 Challenges and Solutions -- 3.5.1 Imbalanced Datasets -- 3.5.2 Ability and Interpretability -- 3.6 Future Trends and Innovations -- 3.6.1 Evolving Technologies -- 3.6.2 Ethical Considerations -- Conclusion -- References -- Part II: AI & -- ML Applications in Threat Detection -- Chapter 4 Comparison Study Between Different Machine Learning (ML) Models Integrated with a Network Intrusion Detection System (NIDS) -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Methodology -- 4.3.1 Data Preprocessing -- 4.3.2 Data Splitting -- 4.3.3 Machine Learning Models -- 4.4 Proposed Model -- 4.5 Experimental Result -- 4.5.1 Performance Evaluation Metrics -- 4.5.2 Results of XGBoost Classifier -- 4.5.2.1 Confusion Matrix -- 4.5.2.2 Accuracy/Recall/Precision -- 4.5.2.3 ROC Curve -- 4.5.3 Results of ExtraTrees Classifier -- 4.5.3.1 Accuracy/Recall/Precision/ROC Curve -- 4.5.4 Comparison and Discussion -- 4.6 Conclusion and Future Work -- References -- Chapter 5 Applications of AI, Machine Learning and Deep Learning for Cyber Attack Detection -- 5.1 Introduction -- 5.1.1 Evolution of Cyber Threats and the Need for Advanced Solutions -- 5.1.2 Taxonomy of Cyber Attacks -- 5.2 Background -- 5.2.1 What is Cyber Security? -- 5.2.2 Cyber Security Systems -- 5.2.3 Ten Different Cyber Security Domains -- 5.3 Role of AI for Cyber Attack Detection -- 5.3.1 Machine Learning for Cyber Attack Detection -- 5.3.2 Deep Learning as a Game Changer in Cyber Attack Detection -- 5.4 Cyber Security Data Sources and Feature Engineering -- 5.4.1 Data Sources -- 5.4.2 Feature Engineering -- 5.5 Training Models for Anomaly Detection in Network Traffic -- 5.5.1 Supervised Learning Models -- 5.5.2 Unsupervised Learning Models -- 5.5.3 Deep Learning Models -- 5.5.4 Hybrid Models.
5.6 Case Study: The Use of AI and ML in Combating Cyber Attacks -- 5.6.1 Analysis: Company X's Strategy for Detecting Cyber Attacks -- 5.6.1.1 Implementation -- 5.6.1.2 Results -- 5.7 Challenges of Artificial Intelligence Applications in Cyber Threat Detection -- 5.8 Future Trends -- 5.9 Conclusion -- References -- Chapter 6 AI-Based Prioritization of Indicators of Intelligence in a Threat Intelligence Sharing Platform -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Methodology -- 6.3.1 Brief Code Explanation -- 6.3.1.1 Bringing in Libraries and Modules -- 6.3.1.2 Parting the Dataset -- 6.3.1.3 Making and Preparing the Model -- 6.3.1.4 Assessing the Model -- 6.3.1.5 Saving the Prepared Model -- 6.3.1.6 Stacking the Prepared Model -- 6.3.1.7 Information Assortment and Preprocessing -- 6.3.1.8 Extricating Remarkable IP Locations -- 6.3.1.9 Creating Highlights for IP Locations -- 6.3.1.10 Stacking Highlights Information -- 6.3.1.11 Foreseeing Needs -- 6.3.1.12 Printing IP Locations and Needs -- 6.3.2 Explanation of the Code Step-By-Step -- 6.4 Proposed Model -- 6.4.1 Workflow Model -- 6.4.2 Decision Tree Machine Learning Model and Its Usage in this Study -- 6.5 Experimental Result/Result Analysis -- 6.6 Conclusion -- 6.6.1 High Level AI Calculations -- 6.6.2 Reconciliation of Regular Language Handling (NLP) Strategies -- 6.6.3 Interpretability and Reasonableness -- 6.6.4 Taking Care of Information Changeability -- 6.6.5 Ill-Disposed Assault Recognition -- 6.6.6 Moral Contemplations -- References -- Chapter 7 Email Spam Classification Using Novel Fusion of Machine Learning and Feed Forward Neural Network Approaches -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Proposed Methodology -- 7.4 Experimentation and Results -- 7.4.1 Data Assortment -- 7.4.2 Applying ML Algorithms -- 7.4.3 Apply FFNN -- 7.4.4 Apply Stacking Ensemble of RF and FFNN.
7.4.5 Apply Voting Ensemble of RF and FFNN -- 7.4.6 Comparison of All Models -- 7.5 Conclusion -- References -- Chapter 8 Intrusion Detection in Wireless Networks Using Novel Classification Models -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Methodology -- 8.4 State of the Art -- 8.5 Result Analysis -- 8.6 Conclusion -- References -- Chapter 9 Detection and Proactive Prevention of Website Swindling Using Hybrid Machine Learning Model -- 9.1 Introduction -- 9.2 Related Literature Survey -- 9.3 Proposed Framework -- 9.3.1 Block Diagram -- 9.3.2 Flow Chart -- 9.4 Implementation -- 9.4.1 Random Forest -- 9.4.2 XGBoost -- 9.4.3 CATBoost -- 9.5 Result Analysis -- 9.6 Conclusion -- References -- Part III: Advanced Security Solutions & -- Case Studies -- Chapter 10 Securing the Future Networks: Blockchain-Based Threat Detection for Advanced Cyber Security -- 10.1 Introduction -- 10.1.1 Background and Evolution of Cybersecurity Threats -- 10.1.2 The Need for Advanced Threat Detection -- 10.1.3 Review of Blockchain Technology in Cybersecurity -- 10.2 Understanding Blockchain Technology -- 10.2.1 Basics of Blockchain -- 10.2.2 Decentralization and Security Features -- 10.2.3 Smart Contracts and their Role in Security -- 10.3 Challenges in Traditional Threat Detection -- 10.3.1 Evolving Nature of Cyber Threats -- 10.3.2 The Importance of Proactive Security Solutions -- 10.4 Integrating Blockchain into Cybersecurity -- 10.4.1 Using Blockchain as the Basis for Improved Security -- 10.4.2 Consensus Mechanisms and Trust -- 10.4.3 Decentralized Identity Management -- 10.5 Challenges and Considerations of Blockchain in Cybersecurity -- 10.5.1 Scalability Issues in Blockchain -- 10.5.2 Regulatory and Compliance Challenges -- 10.5.3 Balancing Transparency and Privacy -- 10.6 Future Trends and Innovations and Case Studies of Blockchain Technology.
10.6.1 Emerging Technologies in Blockchain-Based Security Cyber Security -- 10.6.2 Industry Initiatives and Collaborations on Blockchain for Cybersecurity Solutions -- 10.7 Conclusion -- References -- Chapter 11 Mitigating Pollution Attacks in Network Coding-Enabled Mobile Small Cells for Enhanced 5G Services in Rural Areas -- 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Proposed Model -- 11.4 Results -- 11.5 Conclusion -- References -- Chapter 12 Enhancing Multi-Access Edge Computing Efficiency through Communal Network Selection -- 12.1 Introduction -- 12.2 Related Work -- 12.3 Existing System -- 12.4 Proposed System -- 12.5 Implementation -- 12.6 Results and Discussion -- 12.7 Conclusion -- 12.8 Future Scope -- References -- Chapter 13 Enhancing Cyber-Security and Network Security Through Advanced Video Data Summarization Techniques -- 13.1 Introduction -- 13.1.1 Overview of Video Summarization -- 13.1.2 Importance of Efficient Video Management -- 13.2 Video Summarization Techniques -- 13.2.1 Clustering-Based Methods -- 13.2.2 Deep Learning Frameworks -- 13.2.3 Multimodal Integration Strategies (Audio, Visual, Textual) -- 13.3 Notable Advanced Techniques -- 13.3.1 SVS_MCO Method and Performance -- 13.3.2 Knowledge Distillation (KDAN Framework) -- 13.3.3 Advanced Models (Query-Based, Audio-Visual Recurrent Networks) -- 13.4 Graph-Based and Unsupervised Summarization -- 13.4.1 Graph-Based Summarization Techniques -- 13.4.2 Unsupervised Summarization Methods (Two- Stream Approach for Motion and Visual Features) -- 13.5 Secure and Multi-Video Summarization -- 13.5.1 Secure Video Summarization -- 13.5.2 Multi-Video Summarization -- 13.6 Advanced Scene and Activity-Based Summarization -- 13.6.1 Scene Summarization -- 13.6.2 Activity Recognition -- 13.7 Performance Benchmarking and Evaluation.
13.7.1 Datasets and Evaluation Metrics (e.g., SumMe, TVSum).
Record Nr. UNINA-9911018961503321
Mohanty Sachi Nandan  
Newark : , : John Wiley & Sons, Incorporated, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui