Search

Browse Subject Areas

For Authors

Submit a Proposal

Next-Generation Systems and Secure Computing

Edited by Subhabrata Barman, Santanu Koley, and Subhankar Joardar
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394228263  |  Hardcover  |  
468 pages
Price: $225 USD
Add To Cart

One Line Description
Next-Generation Systems and Secure Computing is essential for anyone looking to stay ahead in the rapidly evolving landscape of technology, as it offers crucial insights into advanced computing models and their security implications, equipping readers with the knowledge needed to navigate the complex challenges of todays digital world.

Audience
Researchers, programmers, practitioners, scholars, developers, scientists, IT professionals, academicians, and students looking for current research on the compatibility of computing models and cyber security methods for the next generation

Description
The development of technology in recent years has produced a number of scientific advancements in sectors like computer science. The advent of new computing models has been one particular development within this sector. New paradigms are always being invented, greatly expanding cloud computing technology. Fog, edge, and serverless computing are examples of these revolutionary advanced technologies. Nevertheless, these new approaches create new security difficulties and are forcing experts to reassess their current security procedures. Devices for edge computing are not designed with the same IT hardware protocols in mind. There are several application cases for edge computing and the Internet of Things (IoT) in remote locations. Yet, cyber security settings and software upgrades are commonly disregarded when it comes to preventing cybercrime and guaranteeing data privacy.
Next-Generation Systems and Secure Computing compiles cutting-edge studies on the development of cutting-edge computing technologies and their role in enhancing current security practices. The book will highlight topics like fault tolerance, federated cloud security, and serverless computing, as well as security issues surrounding edge computing in this context, offering a thorough discussion of the guiding principles, operating procedures, applications, and unexplored areas of study. Next-Generation Systems and Secure Computing is a one-stop resource for learning about the technology, procedures, and individuals involved in next-generation security and computing.

Back to Top
Author / Editor Details
Subhabrata Barman is an assistant professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, West Bengal, India with over 19 years of teaching and research experience. He has edited a number of internationally published books and journals. Additionally, he is a professional member of the Computer Society of India, Institute for Electrical and Electronics Engineers, International Association of Computer Science and Information Technology, and the International Association of Engineers. His research interests include wireless networks, computational intelligence, remote sensing and geoinformatics, precision agriculture, and parallel and grid computing.

Santanu Koley, PhD is a professor in the Computer Science and Engineering Department at Haldia Institute of Technology, West Bengal, India with more than nineteen years of teaching experience and more than eighteen years of research experience. He has published over 50 research publications in numerous national and international journals, conferences, books, and book chapters. His main areas of research include machine learning, cloud computing, digital image processing, and artificial intelligence.

Subhankar Joardar, PhD is a professor and head of the Department of Computer Science and Engineering, Haldia Institute of Technology, India. He has published over 20 technical papers in referred journals and conferences. Additionally, he has served as an organizing chair and program committee member for several international conferences and is a member of the Computer Society of India. His current research interests include swarm intelligence, routing in mobile ad hoc networks, and machine learning.

Back to Top

Table of Contents
Preface
1. Yet Another Move Towards Securing Video Using Sudoku-Fernet
Sunanda Jana, Swarnajit Bhattacharya, Mrinmoy Sen, Abhinandan Khan, Arnab Kumar Maji and Rajat Kumar Pal
1.1 Introduction
1.2 Literature Survey
1.3 Proposed Methodology
1.3.1 Proposed Algorithm for Generating Sudoku-Fernet Cipher Key
1.3.2 Encryption Process
1.4 Result Analysis
1.5 Computational Complexity
1.6 Conclusions
References
2. Watermarking: Characteristics, Methods, and Evaluation
Soumitra Roy and Bappaditya Chakraborty
2.1 Introduction
2.1.1 Chapter Organization
2.2 Watermark Definition
2.2.1 Digital Watermarking Applications
2.2.1.1 Copyright Protection
2.2.1.2 Fingerprinting
2.2.1.3 Broadcast Monitoring
2.2.1.4 Tamper Proofing
2.3 Properties of Watermarking
2.4 Categorization of Watermarking
2.4.1 Related Works on Watermarking
2.5 Attacks on Watermarking
2.5.1 Enhancement Technique Attacks
2.5.2 Noise Addition Attacks
2.5.3 Geometric Transformation Attacks
2.5.4 Compression Attack
2.5.5 Combinational Attacks
2.6 Chapter Summary
References
3. A Comprehensive Study on Deep Learning and Artificial Intelligence for Malware Analysis
Tukkappa Gundoor and Sridevi
3.1 Introduction
3.2 The Evolving Landscape of Malware Threats
3.2.1 Polymorphic and Metamorphic Malware
3.2.2 Advanced Persistent Threats (APTs)
3.2.3 Fileless and Memory-Based Attacks
3.2.4 Ransomware and Cryptojacking
3.2.5 Supply Chain Attacks
3.2.6 IoT and Mobile Malware
3.2.7 Zero-Day Exploits
3.3 The Role of Deep Learning and AI in Enhancing Cybersecurity
3.3.1 Advanced Threat Detection
3.3.2 Real-Time Response and Mitigation
3.3.3 Behavioral Analysis
3.3.4 Anomaly Detection
3.3.5 Predictive Security
3.3.6 Reducing False Positives
3.3.7 Continuous Learning and Improvement
3.4 Deep Learning Models for Malware Analysis
3.4.1 Convolutional Neural Networks (CNNs)
3.4.2 Recurrent Neural Networks (RNNs) for Malware Analysis
3.4.3 Long Short-Term Memory Networks (LSTMs)
3.4.4 Generative Adversarial Networks (GANs)
3.4.5 Radial Basis Function Networks (RBFNs)
3.4.6 Deep Belief Networks (DBNs)
3.5 AI Techniques in Malware Analysis
3.5.1 Unsupervised Learning
3.5.2 Supervised Learning
3.5.3 Deep Learning
3.6 Challenges and Limitations in Malware Family Classification
3.6.1 Lack of Labeled Data
3.6.2 Imbalanced Data
3.6.3 Feature Engineering
3.6.4 Adversarial Attacks
3.6.5 Generalization to New Variants
3.6.6 Real-Time Analysis
3.6.7 Interpretability
3.6.8 False Positives and False Negatives
3.6.9 Overfitting
3.7 Future Directions
3.7.1 Conclusion
References
4. Transmit Texts Covertly Using Trigonometric Functions and Pythagorean Theorem
Nagadevi Bala Nagaram, R. Narmada Devi and S. Karpagam
4.1 Introduction
4.2 Mainstream Definition
4.2.1 Plain Text
4.2.2 Cipher Text
4.2.3 Cipher
4.2.4 Encryption
4.2.5 Decryption
4.2.6 Trigonometric Functions
4.2.7 Pythagorean Theorem
4.3 Description of the Work
4.3.1 Algorithm for Encryption
4.3.2 Numerical Simulation for Encryption Process
4.4 Algorithm for Decryption
4.4.1 Numerical Simulation for the Decryption Process
4.5 Conclusion
References
5. Exploring the Synergy of Cybersecurity and Blockchain: Strengthening Digital Defenses
Mohan Kumar Dehury, Bhabendu Kumar Mohanta, Manorama Patnaik, Biresh Kumar and Purushottam Kumar
5.1 Introduction
5.2 Blockchain Infrastructure
5.2.1 Proof of Work (PoW)
5.2.2 Proof of Stake (PoS)
5.2.3 Delegated Proof of Stake (DPoS)
5.2.4 Proof of Activity (PoA)
5.2.5 Proof of Authority (PoA)
5.2.6 Proof of Burn (PoB)
5.2.7 Proof of Capacity/Proof of Space (PoC/PoSpace)
5.2.8 Proof of Elapsed Time (PoET)
5.2.9 Proof of History (PoH)
5.2.10 Proof of Importance (PoI)
5.3 Literature Review
5.4 Cybersecurity Fundamentals
5.4.1 Threats, Attacks, and Vulnerabilities
5.4.2 Network Protection
5.4.3 History of Cyber Crime and Security
5.4.4 Principle of Information Security
5.4.5 Why Cyber Attacks are Increasing
5.4.5.1 Attacks Caused by Hardware Deficiencies
5.4.5.2 Attacks Caused by Software-Based Bugs
5.4.5.3 Attacks Caused by Vulnerabilities in Computer Networks
5.5 Synergies Between Blockchain and Cybersecurity
5.6 Applications of Blockchain and Cybersecurity
5.7 Challenges and Considerations
5.8 Future Directions and Innovations
5.9 Conclusion
References
6. Protecting in the Digital Age: A Comprehensive Examination of Cybersecurity and Legal Implications
Nazeer Shaik, B. Hari Chandana, P. Chitralingappa and C. Sasikala
6.1 Introduction
6.1.1 Introduction to Legal Issues in Cybersecurity
6.1.2 Importance of Understanding the Legal Landscape in Cybersecurity
6.2 First-Order Heading
6.2.1 Second-Order Heading
6.2.2 Data Breach Notification Requirements and Regulations
6.2.3 Identity Theft Laws and Countermeasures
6.2.4 Computer Fraud and Online Scams
6.3 Data Protection and Privacy Laws
6.3.1 Introduction to Data Protection and Privacy Regulations
6.3.1.1 An Introduction to Data Protection and Privacy
6.3.2 General Data Protection Regulation (GDPR)
6.3.3 The California Consumer Privacy Act (CCPA)
6.3.4 Other Notable Data Protection Laws and Frameworks
6.4 Intellectual Property Rights in Cyberspace
6.5 Cybersecurity Regulations and Compliance
6.6 Cybersecurity Incident Response and Reporting
6.7 International Laws and Jurisdiction in Cybersecurity
6.7.1 International Treaties and Co-Operation in Addressing Cybercrime
6.7.2 Mutual Legal Assistance and Extradition Processes
6.8 Liability and Responsibility in Cybersecurity
6.9 Government Surveillance and Cybersecurity
6.10 Cybersecurity and Employment Law
6.11 Cybersecurity and E-Commerce
6.12 Emerging Legal Issues in Cybersecurity
6.13 Result
6.14 Conclusion
References
7. A Novel Non-Orthogonal Multiple Access Scheme for Next Generation Millimeter-Wave 5G Communications
Udayakumar Easwaran and Krishnaveni Vellingiri
7.1 Introduction
7.2 Related Works
7.3 MIMO–NOMA Systems
7.4 Phase Noise
7.5 Results and Discussion
7.6 Conclusion
References
8. Generation of Key Predistribution Scheme Applying Quasi-Symmetric Designs and Bent Functions in the Wireless Sensor Network
Debashis Ghosh
8.1 Introduction
8.1.1 Motivation
8.2 Background
8.2.1 Quasi-Symmetric 2-Design
8.2.1.1 Definition
8.2.1.2 Definition
8.2.1.3 Definition
8.2.1.4 Definition
8.2.1.5 Remark
8.2.1.6 Remark
8.2.1.7 Definition
8.2.1.8 Lemma
8.2.1.9 Example
8.2.1.10 Example
8.2.1.11 Lemma
8.2.1.12 Theorem (Fisher’s Inequality)
8.2.1.13 Definition
8.2.1.14 Result
8.2.1.15 Definition
8.2.1.16 Example
8.2.1.17 Example
8.2.1.18 Example
8.2.1.19 Theorem
8.2.1.20 Theorem
8.2.1.21 Example
8.2.2 Strongly Regular Graph
8.2.2.1 Lemma
8.2.2.2 Theorem
8.2.2.3 Example
8.2.2.4 Theorem
8.2.3 Background of Key Predistribution Scheme
8.2.4 Bent Functions
8.2.4.1 Definition
8.2.4.2 Definition
8.2.5 Association Between Key Allocation Employing Quasi-Symmetric Designs
8.3 Our Proposed Scheme
8.3.1 Network Generation
8.3.1.1 Lemma
8.3.1.2 Theorem
8.3.1.3 Remark
8.3.2 Algorithm of the Proposed Key Predistribution Protocol
8.3.3 Calculation of Storage Overhead of the Proposed Scheme
8.3.4 Measurement of Network Scalability of the Proposed Scheme
8.3.5 Session Key Sharing Scheme of the Proposed Methodology
8.3.6 Resiliency Against Random Node Compromise of the Proposed Scheme
8.4 Conclusion
References
9. Enhanced Security Measures Within the ITS Infrastructure Through the Application of Machine Learning Algorithms for Anomaly Detection
Shiplu Das, Soumi De, Ananya Ghosh, Sovraj Dey and Tania Bhattacharjee
9.1 Introduction
9.2 Literature Review
9.2.1 Development of Intelligent Transportation
9.2.2 Security Threats in ITS
9.2.3 Privacy Concerns in ITS
9.3 Proposed Work
9.4 Methodology Analysis and Discussion
9.5 Conclusion
References
10. The Impact of Distributed Ledger in IoT: A Comprehensive Overview
Rick Hore, Rishav Dan, Abhijit Sarkar and Sabyasachi Samanta
10.1 Introduction
10.1.1 Distributed Ledger System
10.1.2 Use of Distributed Ledger Together with IoT
10.1.3 Advantages of IoT-Powered DLT
10.1.4 Disadvantages of IoT-Powered DLT
10.2 Related Work
10.3 The Potential of DTL in IoT Application
10.4 Current Use Cases of IoT and DLT
10.5 Opportunities and Challenges of Integrating DLT with IoT
10.5.1 Opportunities
10.5.2 Challenges
10.6 The Future of DLT in IoT Ecosystems
10.7 Conclusion
References
11. A Cryptographic Technique Using Chemicals and Graphs
Kala Raja Mohan, Nagadevi Bala Nagaram, R. Narmada Devi, Regan Murugesan
and Subashini Chandrasekar
11.1 Introduction
11.2 Standard Definitions
11.3 Periodic Table
11.4 Coding Table with Chemical Elements
11.5 Encryption Algorithm
11.6 Encryption Process—Example
11.7 Algorithm for Decryption
11.8 Decryption Process-Example
11.9 Conclusion
References
12. Federated Learning: A Secure Distributed Machine Learning Approach for IoT Technology
Rituparna Saha and Amit Biswas
12.1 Introduction
12.1.1 Overview of Federated Learning
12.1.2 Differences Between Distributed Learning and Federated Learning
12.1.3 Main Advantages of FL
12.1.4 Core Challenges of FL
12.1.4.1 Communication Overhead
12.1.4.2 System Heterogeneity
12.1.4.3 Heterogeneous Data
12.1.4.4 Privacy and Security Risks
12.2 Categorization of FL
12.2.1 Data Partitioning
12.2.1.1 Horizontal FL
12.2.1.2 Vertical FL
12.2.1.3 Federated Transfer Learning
12.3 Data Availability
12.3.1 Cross-Silo FL
12.3.2 Cross-Device FL
12.4 Federated Learning Training Approaches
12.5 Key Research Directions Related to FL
12.5.1 Privacy and Security
12.5.2 Communication Overhead
12.5.3 Data Heterogeneity
12.6 Application Areas of FL
12.6.1 Healthcare
12.6.1.1 Predictive Model
12.6.1.2 Personalized Medicine
12.6.1.3 Drug Discovery
12.6.1.4 Disease Prediction
12.6.1.5 Medical Image Analysis
12.6.1.6 Remote Patient Monitoring
12.6.1.7 Healthcare Fraud Detection
12.6.2 Agriculture
12.6.2.1 Crop Yield Prediction
12.6.2.2 Disease Prediction
12.6.2.3 Precision Agriculture
12.6.2.4 Soil Health Monitoring
12.6.2.5 Crop Variety Recommendation
12.6.2.6 Supply Chain Optimization
12.6.3 Smart City
12.6.3.1 Traffic Management
12.6.3.2 Public Transportation
12.6.3.3 Air Quality Monitoring
12.6.3.4 Waste Management
12.6.3.5 Security
12.6.3.6 Urban Planning
12.7 Conclusion
References
13. Security Analysis for Mobile Crowdsensing Scheme by Predicting Vehicle Mobility Using Deep Learning Algorithm
Monojit Manna, Arpan Adhikary and Sima Das
13.1 Introduction
13.2 Related Work
13.2.1 Crowdsensing Through Mobile Node
13.2.2 Vehicle Trajectory Prediction
13.3 System Model
13.3.1 Opportunistic Communication Approach for Smart Cities
13.3.2 Efficient Data Center Strategies for Sensory Data Collection
13.3.3 Optimizing Surrounding Data Sensing in Smart Cities
13.4 Model of Threat in Mobile Crowdsensing
13.4.1 Spoofing
13.4.2 Sybil
13.4.3 Privacy Leakage
13.4.4 Faked Sensing Attacks
13.4.5 Jamming
13.4.6 DoS
13.4.7 Advanced Persistent Threat (APT)
13.4.8 Smart Attacks
13.5 DL-Based Authentication
13.6 Dl-Based Privacy Protection
13.7 False Sensing Countermeasures Based on DL
13.8 Dl-Based Detection of Intrusion
13.9 The DLMV Approach’s Design
13.9.1 Offline Training
13.9.2 Online Recruitment
13.10 Experimental Result
13.11 Conclusion
References
14. A Study on Protection of Multimedia System Contents Using a Biometric-Based Encryption Technique
Pinaki Pratim Acharjya, Santanu Koley, Subhabrata Barman, Subhankar Joardar and Jayeeta Majumder
14.1 Introduction
14.2 Literature Survey
14.3 Multimedia Content Protection
14.4 Encryption/Decryption in Biometrics
14.4.1 Digital Enrollment
14.4.2 Biometric Verification
14.4.3 Password Management
14.4.4 Encryption/Decryption Scheme
14.4.4.1 No Holding of Biometric Image or Template
14.4.4.2 Improved Authentication and Security
14.4.4.3 Personal Data and Communications are More Secure
14.4.4.4 Greater Public Acceptance, Confidence, and Use Greater Compliance
with Privacy Laws
14.4.5 Suitable for Large-Scale Applications
14.5 The Process
14.6 Experimental Results
14.7 Conclusion
References
15. Deep Learning Algorithms for Detecting Network Attacks—An Overview
R. Mythili and A.S. Aneetha
15.1 Introduction
15.1.1 Cybercrime and Cybersecurity
15.1.2 Network Security
15.2 Technologies of Network Security
15.2.1 Intrusion Detection Systems
15.2.1.1 Types of IDS Tools
15.2.1.2 IDS Functionality
15.2.1.3 Implementation of IDS
15.2.2 Malware Detection System (MDS)
15.2.2.1 Types of Malwares
15.2.2.2 Malware Detection Techniques
15.3 Network Attacks
15.3.1 Active Attacks
15.3.2 Passive Attacks
15.4 Deep Learning Approaches
15.4.1 Feed Forward Neural Network (FFNN)
15.4.1.1 In Malware Detection
15.4.2 Convolutional Neural Network (CNN)
15.4.2.1 Implementation of CNN in IDS
15.4.3 Recurrent Neural Networks (RNN)
15.4.3.1 Implementation of RNN in IDS
15.4.4 Autoencoder (AE)
15.4.4.1 Types of AE
15.4.4.2 Autoencoder in IDS
15.4.5 Restricted Boltzmann Machine (RBM)
15.4.5.1 Working of RBM
15.4.5.2 RBM in Network Intrusion Detection
15.5 Models of IDS
15.6 IDS Datasets
15.7 Result Analysis
15.8 Evaluation Metrics
15.8.1 Confusion Matrix
15.8.2 Key Classification Metrics
15.8.3 Metrics Used in IDS
15.9 Conclusion
References
16. Deep Learning Techniques for Detection of Fake News in Social Media with Huge Data
Namratha M., Rajeshwari B. S. and Jyothi S. Nayak
16.1 Introduction
16.2 Related Work
16.3 Proposed Work
16.3.1 Deep Learning Model
16.3.2 Long Short Term Memory (LSTM)-Based Model
16.3.3 Natural Language Processing (NLP) Model
16.4 Results and Discussion
16.5 Conclusion
16.6 Future Work
References
17. A Secure IoT-Based Heart Rate Monitoring and Analyzing System
Soumya Roy, Rajib Manna, Sabyasachi Samanta, Moumita Sahoo and Somak Karan
17.1 Introduction
17.2 Literature Review
17.3 Methodology
17.3.1 Hardware Description
17.3.1.1 ESP32
17.3.1.2 MAX30100
17.3.1.3 Boost Converter
17.3.1.4 Display (OLED)
17.3.1.5 SMS Facility
17.3.2 Application Development
17.3.3 Encryption Technique Description
17.3.3.1 Encryption Process
17.3.3.2 Decryption Process
17.3.4 Software Description
17.3.4.1 Extracted Feature-Based Dataset
17.3.4.2 Data Preparation
17.3.4.3 Splitting of Training and Testing Dataset
17.3.4.4 Machine Learning-Based Algorithms
17.3.4.5 Hyperparameter Tuning Using Grid Search
17.4 Result Analysis
17.4.1 Hardware Implementation
17.4.2 Software Validation
17.4.2.1 Confusion Matrix and Performance Metrics
17.4.2.2 Graphical Performance Analysis
17.5 Conclusion
References
18. A Secure IoT-Based Approach for Smart Irrigation System Using an Arduino Uno Microcontroller
Nitesh Kumar, Soumen Ghosh, Sabyasachi Samanta, Abhijit Sarkar and Priyatosh Jana
18.1 Introduction
18.2 Literature Review
18.3 Methodology
18.3.1 Working of Moisture Sensor
18.3.2 Implementation of Security in an IoT-Based Smart Irrigation System
18.4 Result Analysis
18.5 Conclusion
18.6 Future Aspect
Acknowledgments
References
19. Machine Learning Applications, Challenges, and Securities for Remote Healthcare: A Systematic Review
Arpan Adhikary, Sima Das, Asit Kumar Nayek, Monojit Manna and Rabindranath Sahu
19.1 Introduction
19.2 Definition of Remote Monitoring of Patients
19.3 Difference Between the Terminologies “Remote Health Care” and “Remote Healthcare”
19.4 Components of the Remote Healthcare System
19.5 Benefits of Remote Healthcare
19.6 Challenges in the Remote Healthcare System
19.7 Application Areas of Machine Learning in the Remote Healthcare System
19.7.1 Medical Image Diagnosis
19.7.2 Robotic Surgery
19.7.3 Personalized Medicine
19.7.4 Drug Development
19.8 The Advantage of Remote Monitoring System
19.8.1 Enhanced Treatment Quality and Performance
19.8.2 High Levels of Support and Education
19.8.3 Patient Assurance
19.8.4 Upgrade the Accessibility to More Needful
19.8.5 Enhance User Engagement in Health Check-Ups
19.9 Important Features and Factors of the Remote Monitoring System
19.9.1 A Bluetooth-Based Monitoring System
19.9.2 A Smartphone-Based Application for Better User Interface
19.9.3 A Cloud-Based Storage
19.9.4 Doctor Side Application for Monitoring
19.10 Sensors Needed for the Wireless Body Area Network (WBAN)
19.11 Challenges of the Wireless Body Area Network (WBAN)
19.11.1 Security and Privacy of Medical Data
19.11.2 Sensor and Technology Compatibility
19.11.3 Secure Challenge and Fog Computing
19.11.4 Comfortability
19.11.5 Availability of Proper Internet
19.12 Machine Learning Solution for Remote Monitoring
19.13 Internet of Things Solution for Remote Monitoring
19.14 Security Solution for the Remote Monitoring
19.15 Conclusion
References
20. Enhancing Video Steganography Security for Cross-Platform Applications: A Focus on High-Definition Formats and Streaming Environments
Santanu Koley and Ankur Kumar
20.1 Introduction
20.2 Video Steganography
20.3 The Compressed Domain
20.4 Coding Concepts
20.5 Temporal Model
20.6 Macroblocks Motion Estimation
20.7 Steganalysis
20.7.1 Steganalysis Techniques
20.7.1.1 Targeted Steganalysis
20.7.1.2 Blind Steganalysis
20.7.1.3 Statistical Steganalysis
20.7.1.4 Video Steganalysis
20.8 Cryptography
20.8.1 Substitution Ciphers
20.8.2 Vigenere Encryption
20.8.3 Symmetric Algorithms
20.8.4 Data Encryption Standard (DES)
20.8.5 Advanced Encryption Standard (AES)
20.8.6 Asymmetric Algorithms
20.8.7 RSA Cryptosystem
20.8.7.1 Encryption Process
20.8.7.2 Decryption Process
20.9 Steganographic Encoder
20.9.1 Steganographic Decoder
20.10 Conclusion
20.11 Future Work
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page