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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Next-Generation Systems and Secure Computing is essential for anyone looking to stay ahead in the rapidly evolving landscape of technology. It offers crucial insights into advanced computing models and their security implications, equipping readers with the knowledge needed to navigate the complex challenges of today’s digital world.
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 aren’t 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, cybersecurity 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.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Yet Another Move Towards Securing Video Using Sudoku-Fernet
1.1 Introduction
1.2 Literature Survey
1.3 Proposed Methodology
1.4 Result Analysis
1.5 Computational Complexity
1.6 Conclusions
References
2 Watermarking: Characteristics, Methods, and Evaluation
2.1 Introduction
2.2 Watermark Definition
2.3 Properties of Watermarking
2.4 Categorization of Watermarking
2.5 Attacks on Watermarking
2.6 Chapter Summary
References
3 A Comprehensive Study on Deep Learning and Artificial Intelligence for Malware Analysis
3.1 Introduction
3.2 The Evolving Landscape of Malware Threats
3.3 The Role of Deep Learning and AI in Enhancing Cybersecurity
3.4 Deep Learning Models for Malware Analysis
3.5 AI Techniques in Malware Analysis
3.6 Challenges and Limitations in Malware Family Classification
3.7 Future Directions
References
4 Transmit Texts Covertly Using Trigonometric Functions and Pythagorean Theorem
4.1 Introduction
4.2 Mainstream Definition
4.3 Description of the Work
4.4 Algorithm for Decryption
4.5 Conclusion
References
5 Exploring the Synergy of Cybersecurity and Blockchain: Strengthening Digital Defenses
5.1 Introduction
5.2 Blockchain Infrastructure
5.3 Literature Review
5.4 Cybersecurity Fundamentals
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
6.1 Introduction
6.2 First-Order Heading
6.3 Data Protection and Privacy Laws
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
6.8 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
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
8.1 Introduction
8.2 Background
8.3 Our Proposed Scheme
8.4 Conclusion
References
9 Enhanced Security Measures Within the ITS Infrastructure Through the Application of Machine Learning Algorithms for Anomaly Detection
9.1 Introduction
9.2 Literature Review
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
10.1 Introduction
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
10.6 The Future of DLT in IoT Ecosystems
10.7 Conclusion
References
11 A Cryptographic Technique Using Chemicals and Graphs
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
12.1 Introduction
12.2 Categorization of FL
12.3 Data Availability
12.4 Federated Learning Training Approaches
12.5 Key Research Directions Related to FL
12.6 Application Areas of FL
12.7 Conclusion
References
13 Security Analysis for Mobile Crowdsensing Scheme by Predicting Vehicle Mobility Using Deep Learning Algorithm
13.1 Introduction
13.2 Related Work
13.3 System Model
13.4 Model of Threat in Mobile Crowdsensing
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
13.10 Experimental Result
13.11 Conclusion
References
14 A Study on Protection of Multimedia System Contents Using a Biometric-Based Encryption Technique
14.1 Introduction
14.2 Literature Survey
14.3 Multimedia Content Protection
14.4 Encryption/Decryption in Biometrics
14.5 The Process
14.6 Experimental Results
14.7 Conclusion
References
15 Deep Learning Algorithms for Detecting Network Attacks—An Overview
15.1 Introduction
15.2 Technologies of Network Security
15.3 Network Attacks
15.4 Deep Learning Approaches
15.5 Models of IDS
15.6 IDS Datasets
15.7 Result Analysis
15.8 Evaluation Metrics
15.9 Conclusion
References
16 Deep Learning Techniques for Detection of Fake News in Social Media with Huge Data
16.1 Introduction
16.2 Related Work
16.3 Proposed Work
16.4 Results and Discussion
16.5 Conclusion
16.6 Future Work
References
17 A Secure IoT-Based Heart Rate Monitoring and Analyzing System
17.1 Introduction
17.2 Literature Review
17.3 Methodology
17.4 Result Analysis
17.5 Conclusion
References
18 A Secure IoT-Based Approach for Smart Irrigation System Using an Arduino Uno Microcontroller
18.1 Introduction
18.2 Literature Review
18.3 Methodology
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
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
19.8 The Advantage of Remote Monitoring System
19.9 Important Features and Factors of the Remote Monitoring System
19.10 Sensors Needed for the Wireless Body Area Network (WBAN)
19.11 Challenges of the Wireless Body Area Network (WBAN)
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
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
20.8 Cryptography
20.9 Steganographic Encoder
20.10 Conclusion
20.11 Future Work
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Definition of the Sudoku instance difficulty level according to the ...
Table 1.2 Minimum possible number of clues, in each row and column of a Sudoku...
Table 1.3 Elements and their mini-grid indices in diamond plus pattern.
Table 1.4 Video sizes (mb) and corresponding encryption times (s).
Table 1.5 Hardware test-bed specification.
Table 1.6 Video clip specification.
Chapter 4
Table 4.1 Natural trigonometric functions.
Table 4.2 Encryption alphabet.
Chapter 7
Table 7.1 Comparison of user index and BER.
Chapter 8
Table 8.1 Parameter allocation between quasi-symmetric designs and key space g...
Table 8.2 Comparison data for the resiliency of the proposed network.
Chapter 9
Table 9.1 Different functions and services of ITS.
Table 9.2 The public perceives factors connected to ICT, ITS, and road transpo...
Table 9.3 T index values and p-values of transportation variable.
Chapter 11
Table 11.1 First coding table with odd atomic numbers.
Table 11.2 Second coding table with even atomic numbers.
Table 11.3 Positions of the plain text.
Table 11.4 Odd positions with its chemical and atomic numbers.
Table 11.5 Even positions with its chemical and atomic numbers.
Table 11.6 Plain text obtained from first coding table.
Table 11.7 Plain text obtained from second coding table.
Table 11.8 Combining positions to form plain text.
Chapter 13
Table 13.1 An overview of DL-based MCS security techniques.
Table 13.2 A summary of the most well-liked deep learning frameworks for mobil...
Chapter 15
Table 15.1 Types of attacks.
Table 15.2 Confusion matrix.
Table 15.3 Various model efficiencies.
Table 15.4 Performance analysis of CNN.
Chapter 16
Table 16.1 Classification report for CLSTM.
Table 16.2 Classification report for RNN.
Chapter 17
Table 17.1 Performance index.
Table 17.2 Confusion matrix of phishing detection.
Chapter 18
Table 18.1 Soil moisture level.
List of Illustrations
Chapter 1
Figure 1.1 An instance of a 2D Sudoku puzzle of size 25 × 25 with clues highli...
Figure 1.2 A solution instance of 2D Sudoku puzzle of size 25 × 25 given in Fi...
Figure 1.3 The
i
th solution of a 25 × 25 Sudoku puzzle and the 25 center eleme...
Figure 1.4 The
i
th solution of a 25 × 25 Sudoku puzzle and the 16 elements for...
Figure 1.5 Video size vs. encryption time.
Chapter 2
Figure 2.1 Basic watermarking steps.
Figure 2.2 Classifications of watermarking.
Figure 2.3 Different reversible watermarking methods.
Chapter 3
Figure 3.1 Convolutional neural networks architecture [40].
Figure 3.2 Recurrent neural networks [41].
Figure 3.3 Long short-term memory networks (LSTMs) [44].
Figure 3.4 Generative adversarial networks (GANs) [44].
Figure 3.5 Radial basis function networks (RBFNs) [44].
Figure 3.6 Deep belief networks (DBNs) [44].
Figure 3.7 Malware detection algorithm life cycle.
Chapter 4
Figure 4.1 The angles in radius and degrees in the unit circle for TR. Source:...
Figure 4.2 Unit circle values of trigonometric functions. Source: https://en.w...
Figure 4.3 A flow chart of cryptography technique.
Figure 4.4 Flow chart of the cryptography technique using trigonometry functio...
Figure 4.5 The QR code for the values 974.4, 999.9, 939.6, 990.3, and 945.4.
Chapter 5
Figure 5.1 The fundamental idea behind blockchain.
Figure 5.2 Layers of blockchain architecture.
Figure 5.3 Consensus mechanism in blockchain technology [13].
Figure 5.4 Principles of information security.
Figure 5.5 Attacks caused by software-based bugs.
Figure 5.6 Startegies for reducing network attacks.
Figure 5.7 Ethereum-based smart contract system.
Chapter 6
Figure 6.1 Technical challenges in mobile security include managing the regist...
Figure 6.2 Enterprise customers focus on the interface between software-as-a-s...
Figure 6.3 Most enterprises do not fully entrust software-as-a-service provide...
Chapter 7
Figure 7.1 System model of cooperative NOMA downlink.
Figure 7.2 Block diagram of MIMO–NOMA systems.
Figure 7.3 BER versus user index.
Chapter 8
Figure 8.1 (7, 3, 1) Balanced incomplete block design.
Figure 8.2 Figure of tf-SRG.
Chapter 9
Figure 9.1 Privacy attacks and privacy challenges.
Figure 9.2 Security measures within the ITS infrastructure through ML algorith...
Chapter 10
Figure 10.1 Centralized ledger system [4].
Figure 10.2 Distributed ledger system [6].
Figure 10.3 Use cases of distributed ledger in IoT [8].
Figure 10.4 IoT system with other implementations [8].
Figure 10.5 Determinants of DLT in IoT [8].
Figure 10.6 Components of a DLT area: an arrow indicates the MQTT-based commun...
Figure 10.7 Centralized ledger system in networking [18].
Figure 10.8 Current use cases of IoT and DLT [28].
Chapter 11
Figure 11.1 Encrypted image.
Chapter 13
Figure 13.1 Threats to mobile crowdsensing systems during information exchange...
Figure 13.2 Mechanism for pre-contracting incentives [11].
Figure 13.3 Threats to mobile crowdsensing systems during information exchange...
Figure 13.4 Experimental result according to dataset.
Figure 13.5 Result analysis.
Chapter 14
Figure 14.1 Structure of the file transfer.
Chapter 15
Figure 15.1 Basic architecture of IDS.
Figure 15.2 Feed forward network architecture.
Figure 15.3 Convolutional neural network architecture.
Figure 15.4 Recurrent neural network architecture.
Figure 15.5 Autoencoder architecture.
Figure 15.6 Restricted boltzmann machine architecture.
Chapter 16
Figure 16.1 Training and testing phase.
Figure 16.2 Deep learning applications.
Figure 16.3 Long short-term memory (LSTM).
Figure 16.4 High-level design.
Figure 16.5 System architecture.
Figure 16.6 Confusion matrix for CLSTM.
Figure 16.7 Confusion matrix for RNN.
Figure 16.8 Accuracy plot graph.
Figure 16.9 Loss plot graph.
Chapter 17
Figure 17.1 Overall block diagram of the whole system.
Figure 17.2 The flow diagram of the overall system.
Figure 17.3 The circuit diagram of the wearable device.
Figure 17.4 ESP32.
Figure 17.5 MAX30100.
Figure 17.6 Boost converter.
Figure 17.7 Organic light-emitting diode (OLED).
Figure 17.8 Basic block diagram of the predictive model for heart abnormality ...
Figure 17.9 Designed hardware circuit.
Figure 17.10 Hardware circuit with OLED display.
Figure 17.11 App-based remote monitoring.
Figure 17.12 Validation of the developed system with a commercial pulse oximet...
Figure 17.13 Activation page.
Figure 17.14 Type of message sent to the registered alert number.
Figure 17.15 Evaluation metrics of the proposed prediction model.
Figure 17.16 Graphical performance analysis with ROC curve of the heart abnorm...
Chapter 18
Figure 18.1 Smart irrigation system.
Figure 18.2 Economic, ecological, and communal benefits of the irrigation stru...
Figure 18.3 Different types of sensors.
Figure 18.4 Threshold of moisture level.
Figure 18.5 LM35 temperature sensor.
Figure 18.6 Flowchart of a smart irrigation system.
Chapter 19
Figure 19.1 The elements of the smart healthcare framework.
Figure 19.2 Architecture of remote monitoring of patients.
Figure 19.3 Architecture of IoT framework for remote healthcare.
Chapter 20
Figure 20.1 Transcode processor chart.
Figure 20.2 Transcode process: decode packet flow chart.
Figure 20.3 Encoder architecture overview.
Figure 20.4 Decoder architecture overview.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106
Advances in Cyber Security
Series Editors: Rashmi Agrawal and D. Ganesh Gopal
Scope: The purpose of this book series is to present books that are specifically designed to address the critical security challenges in today’s computing world including cloud and mobile environments and to discuss mechanisms for defending against those attacks by using classical and modern approaches of cryptography, blockchain and other defense mechanisms. The book series presents some of the state-of-the-art research work in the field of blockchain, cryptography and security in computing and communications. It is a valuable source of knowledge for researchers, engineers, practitioners, graduates, and doctoral students who are working in the field of blockchain, cryptography, network security, and security and privacy issues in the Internet of Things (IoT). It will also be useful for faculty members of graduate schools and universities. The book series provides a comprehensive look at the various facets of cloud security: infrastructure, network, services, compliance and users. It will provide real-world case studies to articulate the real and perceived risks and challenges in deploying and managing services in a cloud infrastructure from a security perspective. The book series will serve as a platform for books dealing with security concerns of decentralized applications (DApps) and smart contracts that operate on an open blockchain. The book series will be a comprehensive and up-to-date reference on information security and assurance. Bringing together the knowledge, skills, techniques, and tools required of IT security professionals, it facilitates the up-to-date understanding required to stay one step ahead of evolving threats, standards, and regulations.
Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected])
Next-Generation Systems and Secure Computing
Edited by
Subhabrata Barman
Santanu Koley
and
Subhankar Joardar
This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and ScrivenerPublishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2025 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-22826-3
Front cover images supplied by Adobe Firefly Cover design by Russell Richardson
Preface
The rapid evolution of technology has brought us to an era where the convergence of systems, computing, and security is no longer an isolated domain, but an integral aspect of every facet of our digital lives. From advanced data analytics and machine learning to decentralized systems and cloud computing, we are witnessing a profound transformation in how we interact with technology, communicate, and conduct business.
Next-Generation Systems and Secure Computing represent the frontier of this digital revolution, shaping not only how we develop and deploy technology, but also how we protect it. The growing complexity of modern systems demands innovative approaches to both functionality and security, ensuring resilience against increasingly sophisticated threats. As we integrate more sophisticated algorithms, artificial intelligence, and distributed architectures into our systems, the stakes in safeguarding data and infrastructure have never been higher.
This book provides an in-depth exploration of these critical topics, with contributions from leading experts in the fields of systems design, cyber security, and computational theory. It offers a comprehensive look at the cutting-edge technologies that are defining the next generation of secure computing systems, from block chain and quantum computing to advanced cryptography and AI-driven security protocols.
By addressing both the theoretical underpinnings and practical applications of secure computing in modern systems, this book aims to equip researchers, practitioners, and students with the knowledge and tools to tackle the challenges of tomorrow’s digital ecosystem. The chapters presented here cover a broad spectrum of topics, from the evolution of security paradigms to novel approaches in securing data, communications, and infrastructures.
The following research topics are well covered in this book:
Video Steganography Watermarking Malware Analysis CryptographyCybersecurity and Block Chain Cybersecurity and law Secured Communication Systems Security in Wireless Networks Security in IoT Systems Security in Mobile Crowd Sensing Multimedia Security Sentiment Analysis in Social Media
We believe that the integration of security at the very foundation of system design, coupled with the innovative solutions presented in this work, will lay the groundwork for the development of resilient, trustworthy, and future-proof systems. As we look ahead to a world increasingly reliant on interconnected technologies, the need for secure, efficient, and scalable computing systems has never been more urgent.
We hope this book serves as both a valuable reference and an inspiring resource for the next generation of technologists, researchers, and innovators working at the intersection of systems engineering and secure computing. Together, we can build a safer, more robust digital future.
1Yet Another Move Towards Securing Video Using Sudoku-Fernet
Sunanda Jana1, Swarnajit Bhattacharya1, Mrinmoy Sen1*, Abhinandan Khan2, Arnab Kumar Maji3 and Rajat Kumar Pal2
1Haldia Institute of Technology, WB, India
2University of Calcutta, Kolkata, India
3North-Eastern Hill University, Meghalaya, India
Abstract
In this era of digital communication and multimedia content sharing, ensuring the security and privacy of sensitive video data is of utmost importance. Symmetric key encryption is a widely used technique for securing video content; however, the generation of secure and unpredictable encryption keys remains a challenge. This study proposes a novel approach that employs Sudoku puzzles as a mechanism for generating symmetric keys. Then, by passing the key through fernet module, the Sudoku-Fernet cipher key was extracted for video encryption. The Sudoku puzzle’s inherent properties of uniqueness, complexity, and nonlinearity make it an ideal candidate for key generation. The proposed method combines the strength of the Giant Sudoku instance of size 25 × 25 with a cryptographic fernet module to enhance the security of video encryption systems, offering an innovative solution to protect sensitive video content without affecting cost and time.
Keywords: Symmetric key, fernet, sudoku-fernet cipher key, video encryption, security
1.1 Introduction
Sudoku puzzle [1] is a popular logic-based number-placement game that has gained worldwide popularity. Its structure consists of an n × n square grid, containing some clues as preassigned, forming a Sudoku puzzle, where n is an integer and √n is an integer. Thus, minigrids are formed with size √n × √n. In each minigrid, each integer between 1 and n appears only once. Standard Sudoku consists of a 9 × 9 grid divided into nine 3 × 3 subgrids. The goal is to fill in the empty cells with digits from 1 to 9, ensuring that each row, column, and subgrid contains every digit exactly once. Sudoku puzzles can be of various sizes and configurations beyond the classic 9 × 9 grid. Some common Sudoku types are based on different grid sizes [10]:
Classic 9
×
9 Sudoku:
This is the standard version of Sudoku, where the puzzle is presented on a 9 × 9 grid divided into nine 3 × 3 subgrids. The objective is to fill in the grid such that each row, column, and 3 × 3 subgrid contains numbers 1 through 9 with no repetition.
Mini Sudoku (4
×
4):
In Mini Sudoku, the puzzle is played on a 4 × 4 grid, divided into four 2 × 2 subgrids. Each row, column, and 2 × 2 subgrid contain numbers 1 through 4.
6 × 6 Sudoku:
In 6 × 6 Sudoku, the grid is 6 × 6 in size and is divided into six 2 × 3 subgrids. Each row, column, and 2 × 3 subgrid must contain numbers 1–6.
Samurai Sudoku:
Samurai Sudoku is a variant that consists of five overlapping 9 × 9 grids. The objective is to fill in the entire arrangement so that each row, column, and 3 × 3 subgrid in each of the five grids contains numbers 1–9.
Hyper Sudoku (4
×
4 regions):
Hyper Sudoku uses a 9 × 9 grid, but the subgrids are irregular and can have different shapes. In addition, there were four 2 × 2 subregions within the grid. The objective was the same as that of the classic Sudoku.
Giant Sudoku:
Giant Sudoku puzzles have larger grids, often ranging from 12 × 12 to 25 × 25, or even larger. Larger grid sizes provide more challenging puzzle-solving experience.
Diagonal Sudoku:
In Diagonal Sudoku, along with the usual rows, columns, and 3 × 3 subgrids, the diagonals must also contain numbers 1 through 9 (or the corresponding numbers for different grid sizes).
Irregular Sudoku:
Irregular Sudoku, also known as Jigsaw Sudoku, has irregularly shaped subgrids instead of standard 3 × 3 boxes. The objective remains the same: each row, column, and irregular subgrid must contain numbers 1 through 9 (or the corresponding numbers for different grid sizes).
Killer Sudoku:
Killer Sudoku combines elements of Sudoku and Kakuro. Kakuro is a crossword number puzzle in which each number word must add up to the number provided as a clue above or to the left of it. In this variant, you are given additional information in the form of “cages” that represent the sum of the numbers within that cage. The objective was to fill the grid with numbers that satisfied the sum constraints for each cage.
3D Sudoku [11] is a variation of the classic Sudoku puzzle that adds an extra dimension to the game. Instead of the usual 9 × 9 grid, 3D Sudoku is played on a 9 × 9 × 9 grid, which means that it has nine 3 × 3 × 3 cubes. The objective is the same as that of traditional Sudoku: fill in the grid so that every row, column, and 3 × 3 × 3 cube contains the numbers 1 through 9 with no repetition.
Figure 1.1 shows a 25 × 25 Sudoku instance where clues are highlighted in red, whereas Figure 1.2 provides a solution to that Sudoku instance. Although Sudoku puzzles are primarily enjoyed as recreational games, they have also found interesting applications in various fields. One such application is video encryption, in which Sudoku-based algorithms can be utilized to secure video content.
Figure 1.1 An instance of a 2D Sudoku puzzle of size 25 × 25 with clues highlighted in red.
Figure 1.2 A solution instance of 2D Sudoku puzzle of size 25 × 25 given in Figure 1.1.
A Sudoku instance can be solved in multiple ways because we can start from any given clue present in the minigrids. However, in contemporary literature, no technique has been described to determine the number of starting cells. The starting cell becomes fascinating to a mathematician only if it follows a minimal route, and the removal or inclusion of a single clue may generate another Sudoku instance for which other new solutions may exist. Moreover, no current technique can determine the minimum number of clues to be provided to the cells. This accounts for the maximum number of variations in Sudoku puzzle instances. The authors in [8] stated that a minimum of 17 clues are needed to ensure that a Sudoku instance, if solvable, has only one unique solution. Therefore, any Sudoku puzzle instance with fewer than 17 givens, if valid, must have more than one solution. However, a valid Sudoku instance may have multiple correct solutions even if the instance includes more than 17 clues. Several techniques exist for the solving a Sudoku puzzle, which differ depending on the difficulty level of the puzzle. According to contemporary literature, the level of difficulty of a Sudoku puzzle is governed by its number of clues [9]. The relationship between the difficulty level of a Sudoku puzzle and the number of clues presented is shown in Table 1.1.
In addition to the number of clues, the position of the empty cells also influences the difficulty level. For any two Sudoku puzzle instances with the same number of clues, the puzzle where the clues are present in clusters/groups is assigned a higher difficulty level than the puzzle with an even distribution of clues. According to the row and column constraints presented in [9], the minimum possible number of clues, in each row and column for different difficulty levels is set as given in Table 1.2.
Sudoku can also be used for video encryption. Sudoku puzzles have a unique property that can be used to scramble and encrypt data. Sudoku puzzles can be used to create a 9 × 9 matrix that can be used to map the pixels of a video frame to new positions. This scrambling process makes it difficult for unauthorized users to decrypt a video without the correct key.
Table 1.1 Definition of the Sudoku instance difficulty level according to the number of given clues.
Difficulty level
Number of clues
1 (Extremely Easy)
>46
2 (Easy)
36–46
3 (Medium)
32–35
4 (Hard)
28–31
5 (Evil)
17–27
Table 1.2 Minimum possible number of clues, in each row and column of a Sudoku instance for different levels of difficulty.
Difficulty level
Minimum possible number of clues in each row and column
1 (Extremely Easy)
05
2 (Easy)
04
3 (Medium)
03
4 (Hard)
02
5 (Evil)
00
There are several different ways to use Sudoku puzzles for video encryption. A common method is to use the Sudoku matrix to create a permutation function. This function can then be applied to the pixels of a video frame to scramble them. Another common method is to use a Sudoku matrix to create a substitution function. This function can then be used to replace pixel values with new values. Sudoku-based video encryption is a relatively new and an emerging field. However, they have enormous potential advantages over other encryption methods. For example, Sudoku-based encryption is relatively simple to implement and does not require specialized hardware or software. In addition, Sudoku-based encryption is highly resistant to attacks.
Here are some of the potential applications of Sudoku-based video encryption:
Securing confidential video communications
Protecting copyrighted video content
Enhancing the security of video surveillance systems
Video encryption involves the process of transforming a video file into ciphertext, which can only be deciphered with a corresponding decryption key. This ensures that unauthorized individuals cannot access or understand video content. Traditional encryption methods often employ complex mathematical algorithms; however, Sudoku-based encryption offers an alternative approach that combines simplicity and security. Sudoku-based encryption provides several advantages. First, it offers a visually pleasing and challenging encryption method that can engage users in solving the Sudoku puzzle to decrypt a video. Second, the simplicity of Sudoku rules and transformations makes them easier to implement and understand than the complex mathematical algorithms used in traditional encryption methods. Finally, Sudoku-based encryption can provide a level of security suitable for certain applications, particularly when combined with other encryption techniques and key management practices. This study used Giant Sudoku to generate the key. As for 25 × 25 Sudoku 7.5 × 1022, possible Sudoku solution grids are there, which is huge. Therefore, extracting a key is impossible for an attacker.
1.2 Literature Survey
Sudoku has enormous applications in the fields of cryptography [12], steganography [2], and encryption of messages, texts, images, audio, and videos. In today’s digital world, securing data from all possible attacks is very crucial. The proposed method works on a video file to transmit the file securely. Data security is of paramount concern in the digital age. As the volume of information exchanged online continues to increase, so do the risks associated with unauthorized access and tampering. Traditional encryption methods rely on complex algorithms and mathematical operations. Many researchers are exploring this area to make today’s digital world more secure.
In 2018, Rupali et al. [6] encrypted a digital video by applying a Simplified Data Encryption Standard and the chaotic map concept. In their work, the RSA algorithm was used to exchange keys. In 2019, Jana et al. [2] developed a video steganographic scheme that uses color videos and hides values in 9 × 9 Sudoku. To hide the video into 9 × 9 Sudoku, every time this 9 × 9 Sudoku needs to be replicated to continue the procedure. The time required for our proposed method is less than that required in their work without compromising video quality.
To protect data in the cloud, especially images, Tyagi et al. [3] developed a hybrid algorithm by combining AES and Fernet incorporating double-level encryption with CNN Auto-Encoders. This model provides a more secure cloud computing model than the other existing models. The original images are processed as input, encrypted/decrypted, converted into bitmap images as outputs that are decrypted by users with ‘key’ when needed. The developed model primarily focuses on pre-processing the original images and passing them to double-level encryption, where the Fernet and Advanced Encryption Standard (AES) algorithms are utilized as hybrid models. Using this two-level encryption method, the image is passed through the CNN and is initially trained. Once the encryptions (Fernet and AES) are successful, the images are stored and accessed through decryption. AES and Fernet algorithms were applied, and the vector image was processed and regenerated as the original image (bitmap) to obtain the output in a secure manner.
In 2021, Haridas et al. [5] developed a Quasigroup Video Encryption Scheme (QuVench). Using this method, near-real-time secure processing of video data was achieved. In this encryption method, compressed video is considered. The authors proposed a novel approach using Sudoku, which offers a unique and intriguing alternative for video encryption. Here, the complexity of key extraction was very high. Therefore, it is very difficult for an attacker to extract an encryption key.
In another study, Matin et al. [7] described the encryption and compression properties of a CCRM camera. In this case, it appears to be a formidable imaging system for applications demanding highly encrypted and compressed data acquisition at high frame rates. The experiments demonstrated that the original data could only be recovered using the encryption key observed by the detector. In their study, the authors introduced an amplitude-encoding technique for the encryption and compression. Introducing the same, the key-space has been significantly extended, and the risk of brute-force attacks on data recovery is substantially reduced.
Recently, research has been conducted on 3D Sudoku because its application can potentially revolutionize different application domains. Various 3D puzzles are being applied in ongoing research, including structural control in metal additive manufacturing [13], historical artifacts [14], image steganography [15], and various security issues.
1.3 Proposed Methodology
In this study, we perform video encryption, which transforms a video file into ciphertext with the help of Sudoku-Fernet. Fernet is a symmetric encryption algorithm [4] that is a part of the cryptography library in Python. It is designed to provide secure and fast encryption and decryption of the data. Fernet uses symmetric key cryptography, in which the same key is used for both encryption and decryption. When using Fernet, a key can be generated using the Fernet.generate_key() method, which produces a random 256-bit key encoded in URL-safe base 64. This generated key is typically used to encrypt and decrypt data. However, Fernet also allows the use a user-generated key instead of a random key. To use a user-generated key with Fernet using Sudoku, we used the correct format where our generated key is a URL-safe, base 64 encoded 256-bit key. Using a user-generated key, we can have more control over the encryption process. However, this work uses the ith solution of a 25 × 25 Sudoku puzzle and ‘diamond-plus’ pattern to produce the elements to form the strongest key. The security of encrypted data depends on the strength and confidentiality of the key. Our proposed method generates a Sudoku-Fernet, which makes data secure because it is very difficult to break. Our key generation algorithm and video encryption process are described in Section 1.3.1.
1.3.1 Proposed Algorithm for Generating Sudoku-Fernet Cipher Key
To generate the 44-byte long key for video data encryption, we consider the ith solution of a 25 × 25 Sudoku puzzle. We derive the first 25 elements (1 byte each) from the ith solution and, to that end, we take center elements from each of the 25 mini-grids. The ith solution and the center elements of the mini-grids are shown in Figure 1.3. The center elements are shown in gray cells and numbered c1 to c25 in the Sudoku grid. The mini-grid index is shown at the top left corner of each mini-grid. The next 16 elements (1 byte each) are derived from the pattern that we address as diamond-plus, as shown in Figure 1.4. The remaining three elements are three special characters (1 byte each). Finally, this 44-byte long key is passed through a fernet for encryption purposes.
Figure 1.3 The ith solution of a 25 × 25 Sudoku puzzle and the 25 center elements of the mini-grids.
Figure 1.4 The ith solution of a 25 × 25 Sudoku puzzle and the 16 elements forming the diamond plus pattern.
The elements derived from the diamond plus pattern are affiliated with the following mini-grids in the ith solution of the Sudoku puzzle, and are described in Table 1.3. All 16 elements were the central elements of the concerned mini-grids. Elements derived from diamond plus pattern are identified with the index range d1 to d16 in the gray region of Figure 1.4, and are numbered from 1 to 16, which is shown at the upper-left corner of each of the mini-grids.
Table 1.3 Elements and their mini-grid indices in diamond plus pattern.
Element index
Mini-grid index
d1
2
d2
4
d3
6
d4
7
d5
8
d6
9
d7
10
d8
12
d9
14
d10
16
d11
17
d12
18
d13
19
d14
20
d15
22
d16
24
Algorithm 1: Algorithm for generating Cipher Key CK for video encryption using Sudoku in Fernet.
Require:ISL, the ith solution of a 2D Sudoku matrix of dimension 25 × 25
Ensure: 44-byte long Cipher Key CK
Definition: In this algorithm, a minigrid is a 5 × 5 matrix; minigrid indices are considered row wise, left to right
Assign: Tag numbers at the top left corner cell of each mini grid
1.3.2 Encryption Process
After the generation of the key, the data are passed through the fernet skeleton. The encryption of the video data is performed using the Fernet object that we have already generated using our Giant Sudoku Fernet cipher key. Before encrypting video data, Fernet applies a sequence of operations to prepare the data for encryption:
Fernet pads the data
: The video data are padded to meet the encryption block-size requirement. Fernet uses the PKCS7 padding scheme, which adds bytes to the input data to ensure that it is a multiple of the encryption block size (in this case, 128 bits or 16 bytes for the AES-128 encryption used by Fernet).
Fernet generates an initialization vector (IV)
: An IV is a random value used as an additional input during the encryption process to ensure that each encryption produces a unique ciphertext, even for the same input. IV is generated securely and is unique for each encryption operation.
Fernet constructs the encryption payload
: The encryption payload consists of the IV, padded video data, and other necessary information required for decryption.
Fernet performs symmetric encryption: Fernet uses AES in Cipher Block Chaining (CBC) mode with a 128-bit key size. The AES algorithm was applied to the encryption payload using a Fernet key. The CBC mode encrypts each block of data with the previous ciphertext block, ensuring that changes in one block affect the encryption of subsequent blocks, thereby enhancing security.
The encrypted ciphertext is returned: The resulting ciphertext obtained by applying AES encryption to the encryption payload, is the encrypted version of the video data.
1.4 Result Analysis
The use of giant Sudoku in the field of encryption is rare, particularly when using a Fernet key. Fernet uses a 44-byte long encryption key. In our study, we derive a 44-byte long encryption key that is passed through a Fernet for video encryption and decryption.
Figure 1.5 Video size vs. encryption time.
To that end, a graph is provided in Figure 1.5, based on the data available in Table 1.4, to judge the feasibility of the video size vs. encryption time. The specifications of the test bed and the video clips taken for the experiment are provided in Tables 1.5 and 1.6.
Table 1.4 Video sizes (mb) and corresponding encryption times (s).
Sl. no.
Video clip size (mb)
Encryption time (s)
1
16.5
1
2
23.1
1
3
31.2
3
4
41.4
3
5
52.6
4
6
64.2
5
7
80.7
6
8
97.8
6
9
113
9
10
135
10
Table 1.5 Hardware test-bed specification.
Hardware resource
Specification
Processor
Intel
®
Core
TM
i5-7200U
CPU speed
2.50 GHz
RAM
8 GB
OS
Windows (64-bit)
Python coding platform
Anaconda 3
Table 1.6 Video clip specification.
Video parameter
Specification
Video codec
AVC
Video encoding type
H264
Audio codec
mp3
Audio encoding type
stereo
Video quality
1,080 p
Audio channel
dual stereo
Video type
Full HD
Audio type
Dolby Atmos 5.1 surround channel
Video frame height
1,920
Video frame width
1,080
1.5 Computational Complexity
The complexity of video encryption using the Fernet cryptographic module depends on several factors, including the size of the video file (n), encryption algorithm used (e.g., AES-128 in Fernet), and underlying implementation details. The time complexity for reading a video file is typically O(n), where n represents the size of the video file. This is because reading the entire file requires iteration over each byte or block of data. The time complexity of the encryption process is influenced by the encryption algorithm used (e.g., AES-128) and block size of the algorithm. For example, if we consider AES-128, which operates on 128-bit blocks (16 bytes), the time complexity for encryption can be expressed as O (n / 16), where 16 is the block size. This indicates that the encryption time increases linearly with the size of the video file but with a smaller constant factor due to the block size. Overall, the time complexity of video encryption using Fernet is O(n), assuming that the encryption process dominates the time complexity.
1.6 Conclusions
The generation of secure and unpredictable encryption keys remains challenging. In this study, a novel approach is proposed that employs Sudoku puzzles as a mechanism for generating symmetric keys. The cipher key is extracted by video encryption by passing the key through the Sudoku-Fernet fernet module, cipher key is extracted for video encryption. This study uses one of the solutions of Giant Sudoku of size 25 × 25, which is very difficult to identify and break owing to its uniqueness and difficult-to-solve property. The proposed method ensures the strength of Sudoku puzzles with cryptographic fernet modules to enhance the security of video encryption systems, thereby offering an innovative solution to protect sensitive video content.
A 25 × 25 Sudoku grid offers a high level of complexity and provides a large key space. This complexity can make it challenging for attackers to guess or brute-force a key. Sudoku involves nonlinear relationships between the numbers in each row, column, and subgrid. This nonlinearity of Giant Sudoku also enhances the security of the key generation process. The filled cells in the Sudoku grid can be considered pseudo-random sequences. This adds an element of unpredictability to the key-generation process. With a 25 × 25 grid, there are many possible ways to arrange numbers, creating a vast number of potential keys. We have used “diamond plus” pattern by applying a new key generation idea. This variability makes it more difficult for attackers to analyze or predict key structures. Thus, this algorithm secures the video in the strongest way by adding extra security to the Fernet cryptographic module during encryption.
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Note
*
Corresponding author
:
2Watermarking: Characteristics, Methods, and Evaluation
Soumitra Roy1* and Bappaditya Chakraborty2
1Dept. of CSE, Dr. Sudhir Chandra Sur Institute of Technology & Sports Complex, Dum Dum, Kolkata, India
2Maulana Abul Kalam Azad University of Technology, Kolkata, India
Abstract
In the last decade, digitization of multimedia data (video, audio, images) is a common factor for the cost, space, and more significantly time effective business models with the swift progression of communication networks. In this counterfeit planet, counterfeiting, forging, alteration, and limitless replication of digital data are produced by illegitimate users with easily available user-friendly software and hardware materials. These criminal-minded people use the modern web network to distribute altered digital data in a smooth, errorless, and economical way. Thus, the main flaw for this transmitted digital data is not have inbuilt inherent security. Recently researchers suggested that this inbuilt inherent security can be provided to digital data if they satisfied confidentiality, integrity, and authenticity features. Stopping unlawful access by illegitimate consumers is confidentiality whereas locating the alteration of digital content by unlawful users is integrity. Authentication prevents countless illicit distributions of copyrighted digital materials. Both the cryptography and steganography mechanisms guaranteed confidentiality features. The foremost shortcoming of the cryptographic and steganographic methods is that the disclosed/decrypted data suffers from counterfeit alteration and plagiarized redistribution of digital data. Nowadays all the security domain researchers recommend that it would be not better but best if security is embedded before the transmission of digital signal not during transmission. This proves that the digital watermarking concept where the watermark is embedded logically or physically into the digital object can easily attain the confidentiality, integrity, and authenticity features. This watermarking research domain initiates with spatial domain watermarking methods where watermark embedding is done by transforming the host image pixel data. Modern researchers logically explained the drawback of these types of schemes where geometric attacks on host images can remove that inserted watermarks. To make the watermark untraceable in the host object watermark is inserted asymmetrically inside transformed objects coefficients for the transformed domain schemes. In conclusion, modern researchers are interested in developing transform domain-based watermarking schemes. During the design of different watermarking schemes, it is found that the cover object is modified permanently which is known as the irreversible watermarking method. Where the cover object is also important like medial and military watermarking techniques reversible watermarking will be a better approach there. Another crucial dispute that arises during designing a watermarking is that an adequate amount of information should be inserted to be robust against small alterations without annihilating the existing watermark in the cover object. A high-capacity watermarking scheme may become visually imperceptible. This conflict fabricates another issue during the design of the watermarking scheme. To evaluate the proposed watermarking algorithms important characteristics are (i) Imperceptibility, (ii) Robustness, and (iii) Embedding capacity.
Keywords: Watermarking, reversible watermarking, irreversible watermarking
2.1 Introduction
In the modern social media and digital era, assuring the copyright protection/authenticity, integrity, and confidentiality of communicated social media content (e.g., Twitter, Facebook, ChatGPT, WeChat, and Ding Talk in the form of audio, video, and images) is of supreme significance. However, this speedily mounting electronic data of social media and relentlessly rationalized data interaction, authenticity, and sheltered communication of these data will, of course, face new problems, as predicted. The swift progress in social media has endorsed images as prominent social media information carriers. On the other hand, throughout the procedure of image communication, images are enormously disposed to a diversity of premeditated/intentional attacks [1], which causes issues in the integrity and authenticity of the communicated image data. Sometimes, it becomes harmful for people’s personal protection, political affairs, and civilization of society, etc., if this premeditated alteration modifies the court proof, medical analysis report, or any other crucial image content. The discussion concludes that ensuring the safety of image communication and maintaining the authenticity of these communicated image resources is a vital crisis that should be addressed [2]. As truthful, multimedia information such as images play a very important role in applications, including health care, military communication, security observation, and education. Owing to the natital security deficiency of open Internet channels, several cryptographic techniques [3] are used to protect digital data while they are being transmitted. The confidentiality of digital data is protected by a number of data encryption or cryptographic algorithms, including the Data Encryption Standard (DES), Advanced Encryption Standard (AES), CAST (named after Carlisle Adams and Stafford Tavares), RSA (named after Ron Rivest, Adi Shamir, and Leonard Adleman), International Data Encryption Algorithm (IDEA), and others. To protect data confidentiality, users typically send sensitive information to approved recipients in encrypted form. Due to the simple accessibility of multimedia modification tools, there is no control in these types of apps to monitor the unauthorized re-sharing of decrypted digital information once it has been unlocked. Security risks in this case are related to integrity, copyright protection, or authentication [3].
Both robust and fragile watermarking techniques are applied to preserve the authenticity, reliability, and integrity of multimedia data [1]. Digital watermarking [5] involves inserting data within the cover image that may later be retrieved or recognized to safeguard the authenticity and/or integrity of the cover object. The legitimacy of the watermarked information is guaranteed by robust watermarking algorithms [4], which makes it impossible to completely erase the copyright data, even after any sort of change to the corresponding watermarked image. However, under a fragile watermarking system, the watermark containing copyright metadata is lost whenever the watermarked image is changed in any way. The watermarked image’s integrity was guaranteed by this feature. Fragile watermarkings [6] are typically employed in important applications such as healthcare imaging and forensic picture preservation, wherein reliability is crucial, given that any tampering to the dataset results in incorrect conclusions or judgments in the medical sector or unlawful offense, respectively. Watermark evidence ought to remain undetectable after being inserted within the cover image, for example, and tampering should be localized even following the watermarked image’s alteration or modification. These are the common requirements for fragile watermarking.
Protecting the ownership rights of digital materials and (ii) maintaining digital content integrity are the two main goals of robust and fragile watermarking. However, the semi-fragile watermarking approach partially complements the traits of robust and fragile watermarking. These types of watermarking may withstand a limited number of attempts at picture manipulation and are suitable for identifying modified image regions.
Figure 2.1 Basic watermarking steps.
2.1.1 Chapter Organization
The remainder of this study is structured as follows. Section 2.2 gives a watermark definition with its various applications. The distinctiveness of the digital watermarking technique is discussed in Section 2.3. Section 2.4 represents a classification of digital watermarking. To evaluate various watermarking systems, Section 2.5 discusses several types of watermarking attacks. This chapter is concluded in Section 2.6.
2.2 Watermark Definition
In general, a digital watermark is unique code that identifies the owner, creator, authorized user, or distributor of a digital file. Digital watermarking is a process in which this digital watermark is inserted imperceptibly into cover objects for applications such as content authentication, copyright protection, illegal copying, fingerprinting, content archiving, broadcast monitoring, distribution, and tampering of digital objects.
