Freshly Printed - allow 7 days lead
Couldn't load pickup availability
Protecting and Mitigating Against Cyber Threats
Deploying Artificial Intelligence and Machine Learning
Sachi Nandan Mohanty (Edited by), Mohanty (Author), Suneeta Satpathy (Edited by), Ming Yang (Edited by), D. Khasim Vali (Edited by)
9781394305223, Wiley
Hardback, published 23 July 2025
560 pages
28 x 19 x 2.5 cm, 0.666 kg
The book provides invaluable insights into the transformative role of AI and ML in security, offering essential strategies and real-world applications to effectively navigate the complex landscape of today’s cyber threats. Protecting and Mitigating Against Cyber Threats delves into the dynamic junction of artificial intelligence (AI) and machine learning (ML) within the domain of security solicitations. Through an exploration of the revolutionary possibilities of AI and ML technologies, this book seeks to disentangle the intricacies of today’s security concerns. There is a fundamental shift in the security soliciting landscape, driven by the extraordinary expansion of data and the constant evolution of cyber threat complexity. This shift calls for a novel strategy, and AI and ML show great promise for strengthening digital defenses. This volume offers a thorough examination, breaking down the concepts and real-world uses of this cutting-edge technology by integrating knowledge from cybersecurity, computer science, and related topics. It bridges the gap between theory and application by looking at real-world case studies and providing useful examples. Protecting and Mitigating Against Cyber Threats provides a roadmap for navigating the changing threat landscape by explaining the current state of AI and ML in security solicitations and projecting forthcoming developments, bringing readers through the unexplored realms of AI and ML applications in protecting digital ecosystems, as the need for efficient security solutions grows. It is a pertinent addition to the multi-disciplinary discussion influencing cybersecurity and digital resilience in the future. Readers will find in this book: Audience Cybersecurity professionals, researchers, academics, industry professionals, technology enthusiasts, policymakers, and strategists interested in the dynamic intersection of artificial intelligence (AI), machine learning (ML), and cybersecurity.
Preface xxi Part I: Foundations of AI & ML in Security 1 1 Foundations of AI and ML in Security 3 Abbreviations 4 1.1 Introduction 4 1.1.1 The Convergence of AI and ML in Security 5 1.2 Understanding Security Attacks 8 1.2.1 Types of Attacks and Vulnerability 9 1.2.2 How Attacks Exploit Vulnerabilities 10 1.2.3 Real-World Examples of AI and ML for Security 10 1.3 Evolution of Information, Cyber Issues/Threats Attacks 11 1.3.1 Cyber Security Threats 13 1.3.2 The Most Prevalent Security Attacks 14 1.4 Machine Learning for Security and Vulnerability 15 1.4.1 Data Collection and Preprocessing 16 1.4.2 Feature Engineering for Security Attack Detection 18 1.5 Challenges and Future Directions 20 1.6 Summary 22 References 23 2 Application of AI and ML in Threat Detection 29 2.1 Introduction 30 2.2 Foundation of AI and ML in Security 32 2.2.1 Definition and Concepts 32 2.2.2 Types of Artificial Intelligence 32 2.2.3 Algorithms and Models in Machine Learning 33 2.3 AI and ML in Applications in Threat Detection 34 2.3.1 Next-Generation Endpoint Protection 34 2.3.2 Endpoint Detection and Response (EDR) 35 2.4 AI/ML Based Network Intrusion Detection Systems (NIDS) 35 2.5 Threat Intelligence and Predictive Analytics 35 2.6 Challenges and Considerations 36 2.7 Integration and Interoperability 36 2.8 Future Directions 37 2.9 Conclusion 37 References 38 3 Artificial Intelligence and Machine Learning Applications in Threat Detection 41 3.1 Introduction 42 3.2 Foundations of Threat Detection 42 3.2.1 Traditional Threat Detection Methods 43 3.2.2 The Need for Advanced Technologies 44 3.3 Overview of AI and ml 44 3.3.1 Understanding Artificial Intelligence 45 3.3.2 Machine Learning Fundamentals 45 3.4 AI and ML Techniques for Threat Detection 46 3.4.1 Supervised Learning and Unsupervised Learning 47 3.4.2 Deep Learning 47 3.5 Challenges and Solutions 48 3.5.1 Imbalanced Datasets 49 3.5.2 Ability and Interpretability 50 3.6 Future Trends and Innovations 51 3.6.1 Evolving Technologies 52 3.6.2 Ethical Considerations 52 Conclusion 53 References 54 Part II: AI & ML Applications in Threat Detection 57 4 Comparison Study Between Different Machine Learning (ML) Models Integrated with a Network Intrusion Detection System (NIDS) 59 4.1 Introduction 60 4.2 Related Work 62 4.3 Methodology 65 4.3.1 Data Preprocessing 65 4.3.2 Data Splitting 66 4.3.3 Machine Learning Models 66 4.4 Proposed Model 67 4.5 Experimental Result 68 4.5.1 Performance Evaluation Metrics 68 4.5.2 Results of XGBoost Classifier 69 4.5.2.1 Confusion Matrix 69 4.5.2.2 Accuracy/Recall/Precision 69 4.5.2.3 ROC Curve 71 4.5.3 Results of ExtraTrees Classifier 71 4.5.3.1 Accuracy/Recall/Precision/ROC Curve 71 4.5.4 Comparison and Discussion 73 4.6 Conclusion and Future Work 74 References 76 5 Applications of AI, Machine Learning and Deep Learning for Cyber Attack Detection 79 5.1 Introduction 80 5.1.1 Evolution of Cyber Threats and the Need for Advanced Solutions 80 5.1.2 Taxonomy of Cyber Attacks 81 5.2 Background 81 5.2.1 What is Cyber Security? 81 5.2.2 Cyber Security Systems 83 5.2.3 Ten Different Cyber Security Domains 85 5.3 Role of AI for Cyber Attack Detection 88 5.3.1 Machine Learning for Cyber Attack Detection 88 5.3.2 Deep Learning as a Game Changer in Cyber Attack Detection 88 5.4 Cyber Security Data Sources and Feature Engineering 89 5.4.1 Data Sources 89 5.4.2 Feature Engineering 90 5.5 Training Models for Anomaly Detection in Network Traffic 91 5.5.1 Supervised Learning Models 91 5.5.2 Unsupervised Learning Models 91 5.5.3 Deep Learning Models 91 5.5.4 Hybrid Models 92 5.6 Case Study: The Use of AI and ML in Combating Cyber Attacks 92 5.6.1 Analysis: Company X’s Strategy for Detecting Cyber Attacks 92 5.6.1.1 Implementation 92 5.6.1.2 Results 93 5.7 Challenges of Artificial Intelligence Applications in Cyber Threat Detection 94 5.8 Future Trends 95 5.9 Conclusion 96 References 96 6 AI-Based Prioritization of Indicators of Intelligence in a Threat Intelligence Sharing Platform 101 6.1 Introduction 102 6.2 Related Work 104 6.3 Methodology 105 6.3.1 Brief Code Explanation 105 6.3.1.1 Bringing in Libraries and Modules 105 6.3.1.2 Parting the Dataset 105 6.3.1.3 Making and Preparing the Model 105 6.3.1.4 Assessing the Model 106 6.3.1.5 Saving the Prepared Model 106 6.3.1.6 Stacking the Prepared Model 106 6.3.1.7 Information Assortment and Preprocessing 106 6.3.1.8 Extricating Remarkable IP Locations 107 6.3.1.9 Creating Highlights for IP Locations 107 6.3.1.10 Stacking Highlights Information 107 6.3.1.11 Foreseeing Needs 107 6.3.1.12 Printing IP Locations and Needs 107 6.3.2 Explanation of the Code Step-By-Step 108 6.4 Proposed Model 111 6.4.1 Workflow Model 111 6.4.2 Decision Tree Machine Learning Model and Its Usage in this Study 112 6.5 Experimental Result/Result Analysis 113 6.6 Conclusion 115 6.6.1 High Level AI Calculations 115 6.6.2 Reconciliation of Regular Language Handling (NLP) Strategies 116 6.6.3 Interpretability and Reasonableness 116 6.6.4 Taking Care of Information Changeability 116 6.6.5 Ill-Disposed Assault Recognition 116 6.6.6 Moral Contemplations 116 References 117 7 Email Spam Classification Using Novel Fusion of Machine Learning and Feed Forward Neural Network Approaches 119 7.1 Introduction 120 7.2 Literature Review 122 7.3 Proposed Methodology 124 7.4 Experimentation and Results 125 7.4.1 Data Assortment 125 7.4.2 Applying ML Algorithms 125 7.4.3 Apply FFNN 127 7.4.4 Apply Stacking Ensemble of RF and FFNN 127 7.4.5 Apply Voting Ensemble of RF and FFNN 127 7.4.6 Comparison of All Models 128 7.5 Conclusion 129 References 130 8 Intrusion Detection in Wireless Networks Using Novel Classification Models 131 8.1 Introduction 132 8.2 Literature Review 133 8.3 Methodology 138 8.4 State of the Art 140 8.5 Result Analysis 142 8.6 Conclusion 144 References 144 9 Detection and Proactive Prevention of Website Swindling Using Hybrid Machine Learning Model 147 9.1 Introduction 148 9.2 Related Literature Survey 148 9.3 Proposed Framework 152 9.3.1 Block Diagram 153 9.3.2 Flow Chart 154 9.4 Implementation 154 9.4.1 Random Forest 155 9.4.2 XGBoost 155 9.4.3 CATBoost 155 9.5 Result Analysis 156 9.6 Conclusion 158 References 158 Part III: Advanced Security Solutions & Case Studies 161 10 Securing the Future Networks: Blockchain-Based Threat Detection for Advanced Cyber Security 163 10.1 Introduction 164 10.1.1 Background and Evolution of Cybersecurity Threats 164 10.1.2 The Need for Advanced Threat Detection 166 10.1.3 Review of Blockchain Technology in Cybersecurity 167 10.2 Understanding Blockchain Technology 169 10.2.1 Basics of Blockchain 170 10.2.2 Decentralization and Security Features 171 10.2.3 Smart Contracts and their Role in Security 172 10.3 Challenges in Traditional Threat Detection 173 10.3.1 Evolving Nature of Cyber Threats 174 10.3.2 The Importance of Proactive Security Solutions 177 10.4 Integrating Blockchain into Cybersecurity 178 10.4.1 Using Blockchain as the Basis for Improved Security 179 10.4.2 Consensus Mechanisms and Trust 181 10.4.3 Decentralized Identity Management 182 10.5 Challenges and Considerations of Blockchain in Cybersecurity 183 10.5.1 Scalability Issues in Blockchain 183 10.5.2 Regulatory and Compliance Challenges 183 10.5.3 Balancing Transparency and Privacy 184 10.6 Future Trends and Innovations and Case Studies of Blockchain Technology 184 10.6.1 Emerging Technologies in Blockchain-Based Security Cyber Security 184 10.6.2 Industry Initiatives and Collaborations on Blockchain for Cybersecurity Solutions 186 10.7 Conclusion 188 References 188 11 Mitigating Pollution Attacks in Network Coding-Enabled Mobile Small Cells for Enhanced 5G Services in Rural Areas 191 11.1 Introduction 192 11.2 Literature Survey 195 11.3 Proposed Model 198 11.4 Results 205 11.5 Conclusion 214 References 214 12 Enhancing Multi-Access Edge Computing Efficiency through Communal Network Selection 219 12.1 Introduction 220 12.2 Related Work 221 12.3 Existing System 222 12.4 Proposed System 225 12.5 Implementation 226 12.6 Results and Discussion 228 12.7 Conclusion 229 12.8 Future Scope 230 References 230 13 Enhancing Cyber-Security and Network Security Through Advanced Video Data Summarization Techniques 233 13.1 Introduction 234 13.1.1 Overview of Video Summarization 234 13.1.2 Importance of Efficient Video Management 235 13.2 Video Summarization Techniques 237 13.2.1 Clustering-Based Methods 240 13.2.2 Deep Learning Frameworks 242 13.2.3 Multimodal Integration Strategies (Audio, Visual, Textual) 248 13.3 Notable Advanced Techniques 249 13.3.1 SVS_MCO Method and Performance 249 13.3.2 Knowledge Distillation (KDAN Framework) 250 13.3.3 Advanced Models (Query-Based, Audio-Visual Recurrent Networks) 251 13.4 Graph-Based and Unsupervised Summarization 252 13.4.1 Graph-Based Summarization Techniques 252 13.4.2 Unsupervised Summarization Methods (Two- Stream Approach for Motion and Visual Features) 252 13.5 Secure and Multi-Video Summarization 253 13.5.1 Secure Video Summarization 254 13.5.2 Multi-Video Summarization 254 13.6 Advanced Scene and Activity-Based Summarization 256 13.6.1 Scene Summarization 256 13.6.2 Activity Recognition 257 13.7 Performance Benchmarking and Evaluation 258 13.7.1 Datasets and Evaluation Metrics (e.g., SumMe, TVSum) 258 13.7.2 Comparative Performance Analysis 260 13.8 Challenges and Future Directions 261 13.8.1 Current Limitations 261 13.8.2 Future Trends 262 13.9 Conclusion 263 References 264 14 Deepfake Face Detection Using Deep Convolutional Neural Networks: A Comparative Study 267 14.1 Introduction 268 14.2 Literature Review 269 14.3 Methodology 272 14.4 Result Analysis 276 14.5 Conclusion 278 14.6 Acknowledgement 278 References 279 15 Detecting Low-Rate DDoS Attacks for CS 283 15.1 Introduction 284 15.2 Requirement Specification 284 15.3 Method and Technologies Involved 285 15.4 Testing and Validation 292 15.5 Results 293 15.6 Conclusion and Future Scope 297 References 297 16 Image Privacy Using Reversible Data Hiding and Encryption 301 16.1 Introduction 302 16.2 Literature Survey 303 16.3 Methodology 305 16.4 Result Analysis 309 16.5 Conclusion 311 Acknowledgment 312 References 312 17 Object Detection in Aerial Imagery Using Object Centric Masked Image Modeling (OCMIM) 315 17.1 Introduction 316 17.2 Literature Review 318 17.3 Methodology 320 17.4 State of the Art 322 17.5 Results Analysis 323 17.5.1 Importing Libraries 323 17.5.2 Datasets 323 17.5.3 Model Comparison 324 17.6 Conclusion 325 Acknowledgment 326 References 326 18 Encryption and Decryption of Credit Card Data Using Quantum Cryptography 331 18.1 Introduction 332 18.1.1 Evolution of Cryptography: A Historical Perspective 332 18.1.2 Quantum Cryptography: Unveiling the Quantum Revolution 333 18.1.3 Quantum Key Distribution Protocols and Practical Implementation 333 18.1.4 Encryption with Quantum Cryptography 333 18.1.5 Decryption with Quantum Cryptography 334 18.1.6 Challenges and Future Prospects 335 18.2 Related Works 335 18.3 Methodology 336 18.3.1 Quantum Key Distribution (QKD) Setup 336 18.3.2 Key Generation and Distribution 337 18.3.3 Encryption 337 18.3.4 Transmission 337 18.3.5 Decryption 337 18.3.6 Aes 338 18.4 Proposed Model 339 18.4.1 Key Generation 339 18.4.2 Encryption 340 18.4.3 Decryption 341 18.5 Experimental Result/Result Analysis 341 18.5.1 Flow Diagram of Quantum Cryptography Encryption and Decryption 341 18.5.2 Algorithm of the Code 343 18.6 Conclusion and Future Work 345 References 346 19 Securing Secrets: Exploring Diverse Encryption and Decryption Through Cryptography with Deep Dive to AES 349 19.1 Introduction 350 19.2 Related Work 353 19.3 Methodology 357 19.4 UML Diagram 359 19.5 Architecture Diagram 360 19.6 Implementation 360 19.7 Conclusion 361 References 362 20 Secure Pass: Hash-Based Password Generator and Checker with Randomized Function 365 20.1 Introduction 366 20.2 Related Work 368 20.3 Methodology 370 20.4 Conclusion and Future Work 376 References 377 21 Beyond Passwords: Face Authentication as a Futuristic Solution for Web Security 379 21.1 Introduction 380 21.1.1 Problem Statement 380 21.1.2 Research Goals 381 21.2 Literature Review 382 21.3 Methodology 386 21.3.1 Face Recognition Algorithms and Techniques 387 21.3.2 Data Collection and Pre-Processing 387 21.3.3 Integration with Web Server Architecture 388 21.4 Proposed Model 389 21.5 Experimental Result/Result Analysis 394 21.5.1 Evaluation and Results 394 21.5.1.1 Performance Metrics for Face Authentication 394 21.5.1.2 Comparative Analysis Utilizing Password-Based Systems 395 21.5.1.3 Evaluation of Usability and User Experience 395 21.5.2 Security and Privacy Considerations 395 21.5.2.1 Implementing Measures to Safeguard Biometric Data 395 21.5.2.2 Vulnerability Analysis and Countermeasures 396 21.5.2.3 Legal and Ethical Considerations 396 21.6 Conclusion and Future Work 396 21.6.1 Contributions and Resulting Effects 397 21.6.2 Areas for Future Research Exploration 397 21.6.3 Implementation Recommendations 397 References 398 22 Cryptographic Key Application for Biometric Implementation in Automobiles 401 22.1 Introduction 402 22.2 Related Work 405 22.3 Methodology 407 22.4 Proposed Methodology 409 22.5 Results and Analysis 414 22.6 Conclusion 415 References 417 23 Password Strength Testing: An Overview and Evaluation 419 23.1 Introduction 420 23.2 Related Work 421 23.3 Methodology 422 23.4 Result 425 23.5 Discussion 426 23.6 Conclusion 427 23.7 Future Work 428 References 429 24 Digital Forensics Analysis on the Internet of Things and Assessment of Cyberattacks 431 24.1 Introduction 432 24.2 Background 433 24.2.1 Relevant Work 434 24.2.2 Cyber Kill Chain 434 24.2.3 SANS Artifacts Categorization 435 24.3 The D4I Framework 436 24.3.1 Mapping and Categorization of Digital Artifacts 436 24.3.2 A Way to Explain in Detail How to Examine and Analyze 437 24.4 Application Illustration 438 24.4.1 Integrating the D4I Framework with IoT Forensics 439 24.5 Discussion 440 24.6 Conclusion 441 References 442 25 Closing the Security Gap: Towards Robust and Explainable AI for Diabetic Retinopathy 445 25.1 Introduction 446 25.2 Security Challenges in AI-Based DR Diagnosis 450 25.2.1 Data Poisoning 450 25.2.2 Adversarial Attacks 451 25.2.3 Privacy Violations 452 25.3 Building Robust and Explainable AI Systems 453 25.3.1 Robust Model Design and Training 453 25.3.2 Data Augmentation to Enhance Model Generalizability 454 25.3.3 Interpretable Deep Learning and Explainable AI 456 25.3.4 Demystifying Deep Learning Predictions 458 25.3.5 Strict Data Governance and Privacy-Preserving Techniques 459 25.3.6 Performance of Strong Data Security Protocols 461 25.4 Benefits of Robust and Explainable AI 464 25.5 Conclusion: The Future of Secure AI in DR Diagnosis 468 References 468 26 Applications of Leveraging Diverse Machine Learning Models for Heart Stroke Prediction and its Security Aspects in Healthcare 473 26.1 Introduction 474 26.2 Literature Review 474 26.3 Approaches 475 26.4 Analysis and Interpretation 477 26.5 Machine Learning and Security Considerations 480 26.6 Suggestions 480 26.7 Conclusion 481 References 482 27 Enhancing Healthcare Security: A Revolutionary Methodology for Deep Learning-Based Intrusion Detection 483 27.1 Introduction 484 27.2 Allied Works 486 27.3 Proposed IDS Approach 488 27.3.1 Data Collection 489 27.3.2 Data Preprocessing 489 27.3.3 Feature Extraction 490 27.3.4 Intrusion Detection Using GRU 490 27.3.4.1 Gated Recurrent Unit 490 27.3.4.2 Optimization of GRU Using ACO Algorithm 492 27.4 Results and Discussion 493 27.4.1 Dataset Description 493 27.4.2 Performance Evaluation 493 27.4.3 Comparative Analysis 496 27.5 Conclusion 497 References 497 28 AI and ML Application in Cybersecurity Hazard Recognition: Challenges, Opportunities, and Future Perspectives in Ethiopia, Horn of Africa 501 28.1 Introduction 502 28.2 AI and ML Application in Cybersecurity Hazard Recognition 504 28.3 Detailed Applications of AI and ML in Ethiopia Perspectives 505 28.3.1 Variance Recognition in Ethiopia 505 28.3.1.1 Probable Challenges in Implementing AI and ML for Variance Recognition in Ethiopia 507 28.3.1.2 Opportunities in Implementing AI and ML Opportunities for Variance Recognition in Ethiopia 508 28.3.2 Intrusion Recognition and Princidenceion Softwares (IDPS) for Hazard Recognition in Ethiopia 510 28.3.2.1 Challenges That Arise When Learning AI and ML-Grounded IDPS Software’s in Ethiopia 511 28.3.2.2 Opportunities in Implementation of AI and ML-Grounded IDPS Software’s in Ethiopia 513 28.3.3 Browser Hijacking Software Recognition in Ethiopia 514 28.3.3.1 Challenges in Browser Hijacking Software Recognition in Ethiopia 516 28.3.3.2 Solutions for Browser Hijacking Software Recognition Challenge in Ethiopia 517 28.4 Scam and Deception Recognition in Ethiopia 518 28.4.1 Challenges in Scam and Deception Recognition in Ethiopia 519 28.4.2 Opportunities of AI and ML Application in Scam and Deception Recognition in Ethiopia 520 28.5 Hazard Acumen Examination in Ethiopia 522 28.5.1 Challenges in Hazard Acumen Examination in Ethiopia 523 28.5.2 AI and ML application in Hazard Acumen Examination in Ethiopia 524 28.6 AI and ML in Cybersecurity: Future Perspectives in Ethiopia 525 28.6.1 Future Perspectives 526 28.7 Conclusion 526 Acknowledgement 527 References 528 Index 531
Sunil Kumar Mohapatra, Ankita Biswal, Harapriya Senapati, Adyasha Swain and Swarupa Pattanaik
Oviya Marimuthu, Priyadharshini Ravi and Senthil Janarthanan
Indu P.V., Preethi Nanjundan and Lijo Thomas
Aryan Kapoor, Jayasankar K.S., Pranay Jiljith, Abishi Chowdhury, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
Chandrakant Mallick, Parimal Kumar Giri, Mamata Garanayak and Sasmita Kumari Nayak
Vijayadharshni, Krishan Shankash, Siddharth Tiwari, Shruti Mishra, Sandeep Kumar Satapathy, Sung-Bae Cho, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
Keshetti Sreekala, Maganti Venkatesh, M. V. Ramana Murthy, S. Venkata Meena, Srinivas Rathula and A. Lakshmanarao
Archith Gandla, Dinesh K., Vasu Gambhirrao, R. M. Krsihna Sureddi, Ramakrishna Kolikipogu and Ramu Kuchipudi
G. Nithish Rao, J.M.S. Abhinav and M. Venkata Krishna Reddy
Adusumalli Balaji, T. Chaitanya, Tirupathi Rao Bammidi, Kanugo Sireesha and Dulam Devee Siva Prasad
Chanumolu Kiran Kumar and Nandhakumar Ramachandran
V. Sahiti Yellanki, B. Venkatesh, N. Sandhya and Neelima Gogineni
Aravapalli Rama Satish and Sai Babu Veesam
Krishna Prasanna Gottumukkala, Sirikonda Manasa, Komal Chakravarthy and Kolikipogu Ramakrishna
P. Venkata Kishore, B. Sivaneasan, Amjan Shaik and Prasun Chakrabarti
Kiranmaie Puvulla, M. Venu Gopalachari, Sreeja Edla, Siddeshwar Vasam and Tushar Thakur
Aarthi Pulivarthi, Jitta Poojitha Reddy, Vanka Eshwar Prabhas, T. Satyanarayana Murthy, Ramesh Babu and Ramu Kuchipudi
Sumit Ranjan, Armaan Munshi, Devansh Gupta, Sandeep Kumar Satapathy, Shruti Mishra, Abishi Chowdhury, Sachi Nandan Mohanty and Mannava Yesu Babu
Yarradoddi Sai Sreenath Reddy, Gurram Thanmai, Kammila Charan Sri Sai Varma, Shruti Mishra, Sandeep Kumar Satapathy, Abishi Chowdhury, Sachi Nandan Mohanty and Mannava Yesu Babu
Aneesh Rathore, Ganesh Choudhary, Mradul Goyal, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
Paras Yadav, Manya Bhardwaj, Akshita Bhamidimarri, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
Priyansh Chatap, Kavish Paul, Akshat Gupta, Sandeep Kumar Satapathy, Sung-Bae Cho, Shruti Mishra, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
Tanmay Agrawal, Kaushal Kanna, Azeem, Abishi Chowdhury, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty
Saswati Chatterjee, Suneeta Satpathy and Pratik Kumar Swain
R. S. M. Lakshmi Patibandla
Busa Shannu Sri, Kotha Dinesh Sai and U. M. Gopal Krishna
M. Priyachitra, Prasanjit Singh, D. Senthil and Ellakkiya Sekar
Shashi Kant and Metasebia Adula
Subject Areas: Computer science [UY]
