Next-Generation Systems and Secure Computing
Description
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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Persons
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, the Institute for Electrical and Electronics Engineers, the 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 19 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.
Content
Preface xxi
1 Yet Another Move Towards Securing Video Using Sudoku-Fernet 1
Sunanda Jana, Swarnajit Bhattacharya, Mrinmoy Sen, Abhinandan Khan, Arnab Kumar Maji and Rajat Kumar Pal
1.1 Introduction 1
1.2 Literature Survey 6
1.3 Proposed Methodology 8
1.4 Result Analysis 12
1.5 Computational Complexity 14
1.6 Conclusions 15
References 15
2 Watermarking: Characteristics, Methods, and Evaluation 17
Soumitra Roy and Bappaditya Chakraborty
2.1 Introduction 18
2.2 Watermark Definition 20
2.3 Properties of Watermarking 22
2.4 Categorization of Watermarking 25
2.5 Attacks on Watermarking 29
2.6 Chapter Summary 32
References 32
3 A Comprehensive Study on Deep Learning and Artificial Intelligence for Malware Analysis 39
Tukkappa Gundoor and Sridevi
3.1 Introduction 40
3.2 The Evolving Landscape of Malware Threats 40
3.3 The Role of Deep Learning and AI in Enhancing Cybersecurity 42
3.4 Deep Learning Models for Malware Analysis 45
3.5 AI Techniques in Malware Analysis 50
3.6 Challenges and Limitations in Malware Family Classification 51
3.7 Future Directions 54
References 55
4 Transmit Texts Covertly Using Trigonometric Functions and Pythagorean Theorem 61
Nagadevi Bala Nagaram, R. Narmada Devi and S. Karpagam
4.1 Introduction 62
4.2 Mainstream Definition 69
4.3 Description of the Work 73
4.4 Algorithm for Decryption 75
4.5 Conclusion 76
References 76
5 Exploring the Synergy of Cybersecurity and Blockchain: Strengthening Digital Defenses 79
Mohan Kumar Dehury, Bhabendu Kumar Mohanta, Manorama Patnaik, Biresh Kumar and Purushottam Kumar
5.1 Introduction 80
5.2 Blockchain Infrastructure 81
5.3 Literature Review 86
5.4 Cybersecurity Fundamentals 87
5.5 Synergies Between Blockchain and Cybersecurity 93
5.6 Applications of Blockchain and Cybersecurity 94
5.7 Challenges and Considerations 95
5.8 Future Directions and Innovations 97
5.9 Conclusion 98
References 99
6 Protecting in the Digital Age: A Comprehensive Examination of Cybersecurity and Legal Implications 105
Nazeer Shaik, B. Hari Chandana, P. Chitralingappa and C. Sasikala
6.1 Introduction 106
6.2 First-Order Heading 107
6.3 Data Protection and Privacy Laws 111
6.4 Intellectual Property Rights in Cyberspace 116
6.5 Cybersecurity Regulations and Compliance 117
6.6 Cybersecurity Incident Response and Reporting 117
6.7 International Laws and Jurisdiction in Cybersecurity 118
6.8 Liability and Responsibility in Cybersecurity 122
6.9 Government Surveillance and Cybersecurity 123
6.10 Cybersecurity and Employment Law 124
6.11 Cybersecurity and E-Commerce 126
6.12 Emerging Legal Issues in Cybersecurity 127
6.13 Result 131
6.14 Conclusion 132
References 133
7 A Novel Non-Orthogonal Multiple Access Scheme for Next Generation Millimeter-Wave 5G Communications 137
Udayakumar Easwaran and Krishnaveni Vellingiri
7.1 Introduction 138
7.2 Related Works 141
7.3 MIMO-NOMA Systems 143
7.4 Phase Noise 150
7.5 Results and Discussion 153
7.6 Conclusion 156
References 156
8 Generation of Key Predistribution Scheme Applying Quasi-Symmetric Designs and Bent Functions in the Wireless Sensor Network 159
Debashis Ghosh
8.1 Introduction 160
8.2 Background 163
8.3 Our Proposed Scheme 178
8.4 Conclusion 182
References 183
9 Enhanced Security Measures Within the ITS Infrastructure Through the Application of Machine Learning Algorithms for Anomaly Detection 187
Shiplu Das, Soumi De, Ananya Ghosh, Sovraj Dey and Tania Bhattacharjee
9.1 Introduction 188
9.2 Literature Review 190
9.3 Proposed Work 197
9.4 Methodology Analysis and Discussion 199
9.5 Conclusion 202
References 203
10 The Impact of Distributed Ledger in IoT: A Comprehensive Overview 205
Rick Hore, Rishav Dan, Abhijit Sarkar and Sabyasachi Samanta
10.1 Introduction 206
10.2 Related Work 212
10.3 The Potential of DTL in IoT Application 215
10.4 Current Use Cases of IoT and DLT 218
10.5 Opportunities and Challenges of Integrating DLT with IoT 220
10.6 The Future of DLT in IoT Ecosystems 222
10.7 Conclusion 224
References 225
11 A Cryptographic Technique Using Chemicals and Graphs 229
Kala Raja Mohan, Nagadevi Bala Nagaram, R. Narmada Devi, Regan Murugesan and Subashini Chandrasekar
11.1 Introduction 230
11.2 Standard Definitions 231
11.3 Periodic Table 232
11.4 Coding Table with Chemical Elements 232
11.5 Encryption Algorithm 233
11.6 Encryption Process-Example 233
11.7 Algorithm for Decryption 235
11.8 Decryption Process-Example 235
11.9 Conclusion 236
References 236
12 Federated Learning: A Secure Distributed Machine Learning Approach for IoT Technology 239
Rituparna Saha and Amit Biswas
12.1 Introduction 240
12.2 Categorization of FL 243
12.3 Data Availability 244
12.4 Federated Learning Training Approaches 244
12.5 Key Research Directions Related to FL 245
12.6 Application Areas of FL 246
12.7 Conclusion 250
References 250
13 Security Analysis for Mobile Crowdsensing Scheme by Predicting Vehicle Mobility Using Deep Learning Algorithm 257
Monojit Manna, Arpan Adhikary and Sima Das
13.1 Introduction 258
13.2 Related Work 259
13.3 System Model 261
13.4 Model of Threat in Mobile Crowdsensing 262
13.5 DL-Based Authentication 264
13.6 Dl-Based Privacy Protection 265
13.7 False Sensing Countermeasures Based on dl 267
13.8 Dl-Based Detection of Intrusion 267
13.9 The DLMV Approach's Design 269
13.10 Experimental Result 272
13.11 Conclusion 273
References 274
14 A Study on Protection of Multimedia System Contents Using a Biometric-Based Encryption Technique 277
Pinaki Pratim Acharjya, Santanu Koley, Subhabrata Barman, Subhankar Joardar and Jayeeta Majumder
14.1 Introduction 277
14.2 Literature Survey 281
14.3 Multimedia Content Protection 283
14.4 Encryption/Decryption in Biometrics 284
14.5 The Process 287
14.6 Experimental Results 289
14.7 Conclusion 290
References 290
15 Deep Learning Algorithms for Detecting Network Attacks-An Overview 293
R. Mythili and A.S. Aneetha
15.1 Introduction 294
15.2 Technologies of Network Security 296
15.3 Network Attacks 299
15.4 Deep Learning Approaches 302
15.5 Models of IDS 311
15.6 IDS Datasets 312
15.7 Result Analysis 314
15.8 Evaluation Metrics 315
15.9 Conclusion 320
References 321
16 Deep Learning Techniques for Detection of Fake News in Social Media with Huge Data 325
Namratha M., Rajeshwari B. S. and Jyothi S. Nayak
16.1 Introduction 326
16.2 Related Work 329
16.3 Proposed Work 332
16.4 Results and Discussion 337
16.5 Conclusion 342
16.6 Future Work 343
References 344
17 A Secure IoT-Based Heart Rate Monitoring and Analyzing System 347
Soumya Roy, Rajib Manna, Sabyasachi Samanta, Moumita Sahoo and Somak Karan
17.1 Introduction 348
17.2 Literature Review 353
17.3 Methodology 355
17.4 Result Analysis 369
17.5 Conclusion 376
References 377
18 A Secure IoT-Based Approach for Smart Irrigation System Using an Arduino Uno Microcontroller 379
Nitesh Kumar, Soumen Ghosh, Sabyasachi Samanta, Abhijit Sarkar and Priyatosh Jana
18.1 Introduction 380
18.2 Literature Review 384
18.3 Methodology 388
18.4 Result Analysis 395
18.5 Conclusion 396
18.6 Future Aspect 396
Acknowledgments 397
References 397
19 Machine Learning Applications, Challenges, and Securities for Remote Healthcare: A Systematic Review 401
Arpan Adhikary, Sima Das, Asit Kumar Nayek, Monojit Manna and Rabindranath Sahu
19.1 Introduction 402
19.2 Definition of Remote Monitoring of Patients 403
19.3 Difference Between the Terminologies "Remote Health Care" and "Remote Healthcare" 404
19.4 Components of the Remote Healthcare System 404
19.5 Benefits of Remote Healthcare 405
19.6 Challenges in the Remote Healthcare System 406
19.7 Application Areas of Machine Learning in the Remote Healthcare System 406
19.8 The Advantage of Remote Monitoring System 408
19.9 Important Features and Factors of the Remote Monitoring System 409
19.10 Sensors Needed for the Wireless Body Area Network (wban) 411
19.12 Machine Learning Solution for Remote Monitoring 413
19.13 Internet of Things Solution for Remote Monitoring 414
19.14 Security Solution for the Remote Monitoring 416
19.15 Conclusion 416
References 417
20 Enhancing Video Steganography Security for Cross-Platform Applications: A Focus on High-Definition Formats and Streaming Environments 421
Santanu Koley and Ankur Kumar
20.1 Introduction 422
20.2 Video Steganography 422
20.3 The Compressed Domain 422
20.4 Coding Concepts 423
20.5 Temporal Model 424
20.6 Macroblocks Motion Estimation 425
20.7 Steganalysis 425
20.8 Cryptography 428
20.9 Steganographic Encoder 435
20.10 Conclusion 437
20.11 Future Work 438
References 438
Index 441
1
Yet 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 Vn is an integer. Thus, minigrids are formed with size Vn × Vn. 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) 17-27Table 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) 00There 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...
