{"product_id":"protecting-and-mitigating-against-cyber-threats-deploying-artificial-intelligence-and-machine-learning-hardback-9781394305223","title":"Protecting and Mitigating Against Cyber Threats; Deploying Artificial Intelligence and Machine Learning (Hardback) 9781394305223","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eProtecting and Mitigating Against Cyber Threats\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eDeploying Artificial Intelligence and Machine Learning\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eSachi Nandan Mohanty (Edited by), Mohanty (Author), Suneeta Satpathy (Edited by), Ming Yang (Edited by), D. Khasim Vali (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394305223, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 23 July 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e560 pages\u003cbr\u003e28 x 19 x 2.5 cm, 0.666 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThe 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eProtecting and Mitigating Against Cyber Threats\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eProtecting and Mitigating Against Cyber Threats\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003eReaders will find in this book: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eProvides comprehensive coverage on various aspects of security solicitations, ranging from theoretical foundations to practical applications;\u003c\/li\u003e \u003cli\u003eIncludes real-world case studies and examples to illustrate how AI and machine learning technologies are currently utilized in security solicitations;\u003c\/li\u003e \u003cli\u003eExplores and discusses emerging trends at the intersection of AI, machine learning, and security solicitations, including topics like threat detection, fraud prevention, risk analysis, and more;\u003c\/li\u003e \u003cli\u003eHighlights the growing importance of AI and machine learning in security contexts and discusses the demand for knowledge in this area.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eCybersecurity professionals, researchers, academics, industry professionals, technology enthusiasts, policymakers, and strategists interested in the dynamic intersection of artificial intelligence (AI), machine learning (ML), and cybersecurity.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Foundations of AI \u0026amp; ML in Security 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Foundations of AI and ML in Security 3\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSunil Kumar Mohapatra, Ankita Biswal, Harapriya Senapati, Adyasha Swain and Swarupa Pattanaik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAbbreviations 4\u003c\/p\u003e \u003cp\u003e1.1 Introduction 4\u003c\/p\u003e \u003cp\u003e1.1.1 The Convergence of AI and ML in Security 5\u003c\/p\u003e \u003cp\u003e1.2 Understanding Security Attacks 8\u003c\/p\u003e \u003cp\u003e1.2.1 Types of Attacks and Vulnerability 9\u003c\/p\u003e \u003cp\u003e1.2.2 How Attacks Exploit Vulnerabilities 10\u003c\/p\u003e \u003cp\u003e1.2.3 Real-World Examples of AI and ML for Security 10\u003c\/p\u003e \u003cp\u003e1.3 Evolution of Information, Cyber Issues\/Threats Attacks 11\u003c\/p\u003e \u003cp\u003e1.3.1 Cyber Security Threats 13\u003c\/p\u003e \u003cp\u003e1.3.2 The Most Prevalent Security Attacks 14\u003c\/p\u003e \u003cp\u003e1.4 Machine Learning for Security and Vulnerability 15\u003c\/p\u003e \u003cp\u003e1.4.1 Data Collection and Preprocessing 16\u003c\/p\u003e \u003cp\u003e1.4.2 Feature Engineering for Security Attack Detection 18\u003c\/p\u003e \u003cp\u003e1.5 Challenges and Future Directions 20\u003c\/p\u003e \u003cp\u003e1.6 Summary 22\u003c\/p\u003e \u003cp\u003eReferences 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Application of AI and ML in Threat Detection 29\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eOviya Marimuthu, Priyadharshini Ravi and Senthil Janarthanan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 30\u003c\/p\u003e \u003cp\u003e2.2 Foundation of AI and ML in Security 32\u003c\/p\u003e \u003cp\u003e2.2.1 Definition and Concepts 32\u003c\/p\u003e \u003cp\u003e2.2.2 Types of Artificial Intelligence 32\u003c\/p\u003e \u003cp\u003e2.2.3 Algorithms and Models in Machine Learning 33\u003c\/p\u003e \u003cp\u003e2.3 AI and ML in Applications in Threat Detection 34\u003c\/p\u003e \u003cp\u003e2.3.1 Next-Generation Endpoint Protection 34\u003c\/p\u003e \u003cp\u003e2.3.2 Endpoint Detection and Response (EDR) 35\u003c\/p\u003e \u003cp\u003e2.4 AI\/ML Based Network Intrusion Detection Systems (NIDS) 35\u003c\/p\u003e \u003cp\u003e2.5 Threat Intelligence and Predictive Analytics 35\u003c\/p\u003e \u003cp\u003e2.6 Challenges and Considerations 36\u003c\/p\u003e \u003cp\u003e2.7 Integration and Interoperability 36\u003c\/p\u003e \u003cp\u003e2.8 Future Directions 37\u003c\/p\u003e \u003cp\u003e2.9 Conclusion 37\u003c\/p\u003e \u003cp\u003eReferences 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Artificial Intelligence and Machine Learning Applications in Threat Detection 41\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIndu P.V., Preethi Nanjundan and Lijo Thomas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 42\u003c\/p\u003e \u003cp\u003e3.2 Foundations of Threat Detection 42\u003c\/p\u003e \u003cp\u003e3.2.1 Traditional Threat Detection Methods 43\u003c\/p\u003e \u003cp\u003e3.2.2 The Need for Advanced Technologies 44\u003c\/p\u003e \u003cp\u003e3.3 Overview of AI and ml 44\u003c\/p\u003e \u003cp\u003e3.3.1 Understanding Artificial Intelligence 45\u003c\/p\u003e \u003cp\u003e3.3.2 Machine Learning Fundamentals 45\u003c\/p\u003e \u003cp\u003e3.4 AI and ML Techniques for Threat Detection 46\u003c\/p\u003e \u003cp\u003e3.4.1 Supervised Learning and Unsupervised Learning 47\u003c\/p\u003e \u003cp\u003e3.4.2 Deep Learning 47\u003c\/p\u003e \u003cp\u003e3.5 Challenges and Solutions 48\u003c\/p\u003e \u003cp\u003e3.5.1 Imbalanced Datasets 49\u003c\/p\u003e \u003cp\u003e3.5.2 Ability and Interpretability 50\u003c\/p\u003e \u003cp\u003e3.6 Future Trends and Innovations 51\u003c\/p\u003e \u003cp\u003e3.6.1 Evolving Technologies 52\u003c\/p\u003e \u003cp\u003e3.6.2 Ethical Considerations 52\u003c\/p\u003e \u003cp\u003eConclusion 53\u003c\/p\u003e \u003cp\u003eReferences 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: AI \u0026amp; ML Applications in Threat Detection 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Comparison Study Between Different Machine Learning (ML) Models Integrated with a Network Intrusion Detection System (NIDS) 59\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAryan Kapoor, Jayasankar K.S., Pranay Jiljith, Abishi Chowdhury, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 60\u003c\/p\u003e \u003cp\u003e4.2 Related Work 62\u003c\/p\u003e \u003cp\u003e4.3 Methodology 65\u003c\/p\u003e \u003cp\u003e4.3.1 Data Preprocessing 65\u003c\/p\u003e \u003cp\u003e4.3.2 Data Splitting 66\u003c\/p\u003e \u003cp\u003e4.3.3 Machine Learning Models 66\u003c\/p\u003e \u003cp\u003e4.4 Proposed Model 67\u003c\/p\u003e \u003cp\u003e4.5 Experimental Result 68\u003c\/p\u003e \u003cp\u003e4.5.1 Performance Evaluation Metrics 68\u003c\/p\u003e \u003cp\u003e4.5.2 Results of XGBoost Classifier 69\u003c\/p\u003e \u003cp\u003e4.5.2.1 Confusion Matrix 69\u003c\/p\u003e \u003cp\u003e4.5.2.2 Accuracy\/Recall\/Precision 69\u003c\/p\u003e \u003cp\u003e4.5.2.3 ROC Curve 71\u003c\/p\u003e \u003cp\u003e4.5.3 Results of ExtraTrees Classifier 71\u003c\/p\u003e \u003cp\u003e4.5.3.1 Accuracy\/Recall\/Precision\/ROC Curve 71\u003c\/p\u003e \u003cp\u003e4.5.4 Comparison and Discussion 73\u003c\/p\u003e \u003cp\u003e4.6 Conclusion and Future Work 74\u003c\/p\u003e \u003cp\u003eReferences 76\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Applications of AI, Machine Learning and Deep Learning for Cyber Attack Detection 79\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eChandrakant Mallick, Parimal Kumar Giri, Mamata Garanayak and Sasmita Kumari Nayak\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 80\u003c\/p\u003e \u003cp\u003e5.1.1 Evolution of Cyber Threats and the Need for Advanced Solutions 80\u003c\/p\u003e \u003cp\u003e5.1.2 Taxonomy of Cyber Attacks 81\u003c\/p\u003e \u003cp\u003e5.2 Background 81\u003c\/p\u003e \u003cp\u003e5.2.1 What is Cyber Security? 81\u003c\/p\u003e \u003cp\u003e5.2.2 Cyber Security Systems 83\u003c\/p\u003e \u003cp\u003e5.2.3 Ten Different Cyber Security Domains 85\u003c\/p\u003e \u003cp\u003e5.3 Role of AI for Cyber Attack Detection 88\u003c\/p\u003e \u003cp\u003e5.3.1 Machine Learning for Cyber Attack Detection 88\u003c\/p\u003e \u003cp\u003e5.3.2 Deep Learning as a Game Changer in Cyber Attack Detection 88\u003c\/p\u003e \u003cp\u003e5.4 Cyber Security Data Sources and Feature Engineering 89\u003c\/p\u003e \u003cp\u003e5.4.1 Data Sources 89\u003c\/p\u003e \u003cp\u003e5.4.2 Feature Engineering 90\u003c\/p\u003e \u003cp\u003e5.5 Training Models for Anomaly Detection in Network Traffic 91\u003c\/p\u003e \u003cp\u003e5.5.1 Supervised Learning Models 91\u003c\/p\u003e \u003cp\u003e5.5.2 Unsupervised Learning Models 91\u003c\/p\u003e \u003cp\u003e5.5.3 Deep Learning Models 91\u003c\/p\u003e \u003cp\u003e5.5.4 Hybrid Models 92\u003c\/p\u003e \u003cp\u003e5.6 Case Study: The Use of AI and ML in Combating Cyber Attacks 92\u003c\/p\u003e \u003cp\u003e5.6.1 Analysis: Company X’s Strategy for Detecting Cyber Attacks 92\u003c\/p\u003e \u003cp\u003e5.6.1.1 Implementation 92\u003c\/p\u003e \u003cp\u003e5.6.1.2 Results 93\u003c\/p\u003e \u003cp\u003e5.7 Challenges of Artificial Intelligence Applications in Cyber Threat Detection 94\u003c\/p\u003e \u003cp\u003e5.8 Future Trends 95\u003c\/p\u003e \u003cp\u003e5.9 Conclusion 96\u003c\/p\u003e \u003cp\u003eReferences 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 AI-Based Prioritization of Indicators of Intelligence in a Threat Intelligence Sharing Platform 101\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVijayadharshni, Krishan Shankash, Siddharth Tiwari, Shruti Mishra, Sandeep Kumar Satapathy, Sung-Bae Cho, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 102\u003c\/p\u003e \u003cp\u003e6.2 Related Work 104\u003c\/p\u003e \u003cp\u003e6.3 Methodology 105\u003c\/p\u003e \u003cp\u003e6.3.1 Brief Code Explanation 105\u003c\/p\u003e \u003cp\u003e6.3.1.1 Bringing in Libraries and Modules 105\u003c\/p\u003e \u003cp\u003e6.3.1.2 Parting the Dataset 105\u003c\/p\u003e \u003cp\u003e6.3.1.3 Making and Preparing the Model 105\u003c\/p\u003e \u003cp\u003e6.3.1.4 Assessing the Model 106\u003c\/p\u003e \u003cp\u003e6.3.1.5 Saving the Prepared Model 106\u003c\/p\u003e \u003cp\u003e6.3.1.6 Stacking the Prepared Model 106\u003c\/p\u003e \u003cp\u003e6.3.1.7 Information Assortment and Preprocessing 106\u003c\/p\u003e \u003cp\u003e6.3.1.8 Extricating Remarkable IP Locations 107\u003c\/p\u003e \u003cp\u003e6.3.1.9 Creating Highlights for IP Locations 107\u003c\/p\u003e \u003cp\u003e6.3.1.10 Stacking Highlights Information 107\u003c\/p\u003e \u003cp\u003e6.3.1.11 Foreseeing Needs 107\u003c\/p\u003e \u003cp\u003e6.3.1.12 Printing IP Locations and Needs 107\u003c\/p\u003e \u003cp\u003e6.3.2 Explanation of the Code Step-By-Step 108\u003c\/p\u003e \u003cp\u003e6.4 Proposed Model 111\u003c\/p\u003e \u003cp\u003e6.4.1 Workflow Model 111\u003c\/p\u003e \u003cp\u003e6.4.2 Decision Tree Machine Learning Model and Its Usage in this Study 112\u003c\/p\u003e \u003cp\u003e6.5 Experimental Result\/Result Analysis 113\u003c\/p\u003e \u003cp\u003e6.6 Conclusion 115\u003c\/p\u003e \u003cp\u003e6.6.1 High Level AI Calculations 115\u003c\/p\u003e \u003cp\u003e6.6.2 Reconciliation of Regular Language Handling (NLP) Strategies 116\u003c\/p\u003e \u003cp\u003e6.6.3 Interpretability and Reasonableness 116\u003c\/p\u003e \u003cp\u003e6.6.4 Taking Care of Information Changeability 116\u003c\/p\u003e \u003cp\u003e6.6.5 Ill-Disposed Assault Recognition 116\u003c\/p\u003e \u003cp\u003e6.6.6 Moral Contemplations 116\u003c\/p\u003e \u003cp\u003eReferences 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Email Spam Classification Using Novel Fusion of Machine Learning and Feed Forward Neural Network Approaches 119\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKeshetti Sreekala, Maganti Venkatesh, M. V. Ramana Murthy, S. Venkata Meena, Srinivas Rathula and A. Lakshmanarao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 120\u003c\/p\u003e \u003cp\u003e7.2 Literature Review 122\u003c\/p\u003e \u003cp\u003e7.3 Proposed Methodology 124\u003c\/p\u003e \u003cp\u003e7.4 Experimentation and Results 125\u003c\/p\u003e \u003cp\u003e7.4.1 Data Assortment 125\u003c\/p\u003e \u003cp\u003e7.4.2 Applying ML Algorithms 125\u003c\/p\u003e \u003cp\u003e7.4.3 Apply FFNN 127\u003c\/p\u003e \u003cp\u003e7.4.4 Apply Stacking Ensemble of RF and FFNN 127\u003c\/p\u003e \u003cp\u003e7.4.5 Apply Voting Ensemble of RF and FFNN 127\u003c\/p\u003e \u003cp\u003e7.4.6 Comparison of All Models 128\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 129\u003c\/p\u003e \u003cp\u003eReferences 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Intrusion Detection in Wireless Networks Using Novel Classification Models 131\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eArchith Gandla, Dinesh K., Vasu Gambhirrao, R. M. Krsihna Sureddi, Ramakrishna Kolikipogu and Ramu Kuchipudi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 132\u003c\/p\u003e \u003cp\u003e8.2 Literature Review 133\u003c\/p\u003e \u003cp\u003e8.3 Methodology 138\u003c\/p\u003e \u003cp\u003e8.4 State of the Art 140\u003c\/p\u003e \u003cp\u003e8.5 Result Analysis 142\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 144\u003c\/p\u003e \u003cp\u003eReferences 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Detection and Proactive Prevention of Website Swindling Using Hybrid Machine Learning Model 147\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eG. Nithish Rao, J.M.S. Abhinav and M. Venkata Krishna Reddy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 148\u003c\/p\u003e \u003cp\u003e9.2 Related Literature Survey 148\u003c\/p\u003e \u003cp\u003e9.3 Proposed Framework 152\u003c\/p\u003e \u003cp\u003e9.3.1 Block Diagram 153\u003c\/p\u003e \u003cp\u003e9.3.2 Flow Chart 154\u003c\/p\u003e \u003cp\u003e9.4 Implementation 154\u003c\/p\u003e \u003cp\u003e9.4.1 Random Forest 155\u003c\/p\u003e \u003cp\u003e9.4.2 XGBoost 155\u003c\/p\u003e \u003cp\u003e9.4.3 CATBoost 155\u003c\/p\u003e \u003cp\u003e9.5 Result Analysis 156\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 158\u003c\/p\u003e \u003cp\u003eReferences 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Advanced Security Solutions \u0026amp; Case Studies 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Securing the Future Networks: Blockchain-Based Threat Detection for Advanced Cyber Security 163\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAdusumalli Balaji, T. Chaitanya, Tirupathi Rao Bammidi, Kanugo Sireesha and Dulam Devee Siva Prasad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 164\u003c\/p\u003e \u003cp\u003e10.1.1 Background and Evolution of Cybersecurity Threats 164\u003c\/p\u003e \u003cp\u003e10.1.2 The Need for Advanced Threat Detection 166\u003c\/p\u003e \u003cp\u003e10.1.3 Review of Blockchain Technology in Cybersecurity 167\u003c\/p\u003e \u003cp\u003e10.2 Understanding Blockchain Technology 169\u003c\/p\u003e \u003cp\u003e10.2.1 Basics of Blockchain 170\u003c\/p\u003e \u003cp\u003e10.2.2 Decentralization and Security Features 171\u003c\/p\u003e \u003cp\u003e10.2.3 Smart Contracts and their Role in Security 172\u003c\/p\u003e \u003cp\u003e10.3 Challenges in Traditional Threat Detection 173\u003c\/p\u003e \u003cp\u003e10.3.1 Evolving Nature of Cyber Threats 174\u003c\/p\u003e \u003cp\u003e10.3.2 The Importance of Proactive Security Solutions 177\u003c\/p\u003e \u003cp\u003e10.4 Integrating Blockchain into Cybersecurity 178\u003c\/p\u003e \u003cp\u003e10.4.1 Using Blockchain as the Basis for Improved Security 179\u003c\/p\u003e \u003cp\u003e10.4.2 Consensus Mechanisms and Trust 181\u003c\/p\u003e \u003cp\u003e10.4.3 Decentralized Identity Management 182\u003c\/p\u003e \u003cp\u003e10.5 Challenges and Considerations of Blockchain in Cybersecurity 183\u003c\/p\u003e \u003cp\u003e10.5.1 Scalability Issues in Blockchain 183\u003c\/p\u003e \u003cp\u003e10.5.2 Regulatory and Compliance Challenges 183\u003c\/p\u003e \u003cp\u003e10.5.3 Balancing Transparency and Privacy 184\u003c\/p\u003e \u003cp\u003e10.6 Future Trends and Innovations and Case Studies of Blockchain Technology 184\u003c\/p\u003e \u003cp\u003e10.6.1 Emerging Technologies in Blockchain-Based Security Cyber Security 184\u003c\/p\u003e \u003cp\u003e10.6.2 Industry Initiatives and Collaborations on Blockchain for Cybersecurity Solutions 186\u003c\/p\u003e \u003cp\u003e10.7 Conclusion 188\u003c\/p\u003e \u003cp\u003eReferences 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Mitigating Pollution Attacks in Network Coding-Enabled Mobile Small Cells for Enhanced 5G Services in Rural Areas 191\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eChanumolu Kiran Kumar and Nandhakumar Ramachandran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 192\u003c\/p\u003e \u003cp\u003e11.2 Literature Survey 195\u003c\/p\u003e \u003cp\u003e11.3 Proposed Model 198\u003c\/p\u003e \u003cp\u003e11.4 Results 205\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 214\u003c\/p\u003e \u003cp\u003eReferences 214\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Enhancing Multi-Access Edge Computing Efficiency through Communal Network Selection 219\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eV. Sahiti Yellanki, B. Venkatesh, N. Sandhya and Neelima Gogineni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 220\u003c\/p\u003e \u003cp\u003e12.2 Related Work 221\u003c\/p\u003e \u003cp\u003e12.3 Existing System 222\u003c\/p\u003e \u003cp\u003e12.4 Proposed System 225\u003c\/p\u003e \u003cp\u003e12.5 Implementation 226\u003c\/p\u003e \u003cp\u003e12.6 Results and Discussion 228\u003c\/p\u003e \u003cp\u003e12.7 Conclusion 229\u003c\/p\u003e \u003cp\u003e12.8 Future Scope 230\u003c\/p\u003e \u003cp\u003eReferences 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Enhancing Cyber-Security and Network Security Through Advanced Video Data Summarization Techniques 233\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAravapalli Rama Satish and Sai Babu Veesam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 234\u003c\/p\u003e \u003cp\u003e13.1.1 Overview of Video Summarization 234\u003c\/p\u003e \u003cp\u003e13.1.2 Importance of Efficient Video Management 235\u003c\/p\u003e \u003cp\u003e13.2 Video Summarization Techniques 237\u003c\/p\u003e \u003cp\u003e13.2.1 Clustering-Based Methods 240\u003c\/p\u003e \u003cp\u003e13.2.2 Deep Learning Frameworks 242\u003c\/p\u003e \u003cp\u003e13.2.3 Multimodal Integration Strategies (Audio, Visual, Textual) 248\u003c\/p\u003e \u003cp\u003e13.3 Notable Advanced Techniques 249\u003c\/p\u003e \u003cp\u003e13.3.1 SVS_MCO Method and Performance 249\u003c\/p\u003e \u003cp\u003e13.3.2 Knowledge Distillation (KDAN Framework) 250\u003c\/p\u003e \u003cp\u003e13.3.3 Advanced Models (Query-Based, Audio-Visual Recurrent Networks) 251\u003c\/p\u003e \u003cp\u003e13.4 Graph-Based and Unsupervised Summarization 252\u003c\/p\u003e \u003cp\u003e13.4.1 Graph-Based Summarization Techniques 252\u003c\/p\u003e \u003cp\u003e13.4.2 Unsupervised Summarization Methods (Two- Stream Approach for Motion and Visual Features) 252\u003c\/p\u003e \u003cp\u003e13.5 Secure and Multi-Video Summarization 253\u003c\/p\u003e \u003cp\u003e13.5.1 Secure Video Summarization 254\u003c\/p\u003e \u003cp\u003e13.5.2 Multi-Video Summarization 254\u003c\/p\u003e \u003cp\u003e13.6 Advanced Scene and Activity-Based Summarization 256\u003c\/p\u003e \u003cp\u003e13.6.1 Scene Summarization 256\u003c\/p\u003e \u003cp\u003e13.6.2 Activity Recognition 257\u003c\/p\u003e \u003cp\u003e13.7 Performance Benchmarking and Evaluation 258\u003c\/p\u003e \u003cp\u003e13.7.1 Datasets and Evaluation Metrics (e.g., SumMe, TVSum) 258\u003c\/p\u003e \u003cp\u003e13.7.2 Comparative Performance Analysis 260\u003c\/p\u003e \u003cp\u003e13.8 Challenges and Future Directions 261\u003c\/p\u003e \u003cp\u003e13.8.1 Current Limitations 261\u003c\/p\u003e \u003cp\u003e13.8.2 Future Trends 262\u003c\/p\u003e \u003cp\u003e13.9 Conclusion 263\u003c\/p\u003e \u003cp\u003eReferences 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Deepfake Face Detection Using Deep Convolutional Neural Networks: A Comparative Study 267\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKrishna Prasanna Gottumukkala, Sirikonda Manasa, Komal Chakravarthy and Kolikipogu Ramakrishna\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 268\u003c\/p\u003e \u003cp\u003e14.2 Literature Review 269\u003c\/p\u003e \u003cp\u003e14.3 Methodology 272\u003c\/p\u003e \u003cp\u003e14.4 Result Analysis 276\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 278\u003c\/p\u003e \u003cp\u003e14.6 Acknowledgement 278\u003c\/p\u003e \u003cp\u003eReferences 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Detecting Low-Rate DDoS Attacks for CS 283\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eP. Venkata Kishore, B. Sivaneasan, Amjan Shaik and Prasun Chakrabarti\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 284\u003c\/p\u003e \u003cp\u003e15.2 Requirement Specification 284\u003c\/p\u003e \u003cp\u003e15.3 Method and Technologies Involved 285\u003c\/p\u003e \u003cp\u003e15.4 Testing and Validation 292\u003c\/p\u003e \u003cp\u003e15.5 Results 293\u003c\/p\u003e \u003cp\u003e15.6 Conclusion and Future Scope 297\u003c\/p\u003e \u003cp\u003eReferences 297\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Image Privacy Using Reversible Data Hiding and Encryption 301\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKiranmaie Puvulla, M. Venu Gopalachari, Sreeja Edla, Siddeshwar Vasam and Tushar Thakur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 302\u003c\/p\u003e \u003cp\u003e16.2 Literature Survey 303\u003c\/p\u003e \u003cp\u003e16.3 Methodology 305\u003c\/p\u003e \u003cp\u003e16.4 Result Analysis 309\u003c\/p\u003e \u003cp\u003e16.5 Conclusion 311\u003c\/p\u003e \u003cp\u003eAcknowledgment 312\u003c\/p\u003e \u003cp\u003eReferences 312\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Object Detection in Aerial Imagery Using Object Centric Masked Image Modeling (OCMIM) 315\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAarthi Pulivarthi, Jitta Poojitha Reddy, Vanka Eshwar Prabhas, T. Satyanarayana Murthy, Ramesh Babu and Ramu Kuchipudi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 316\u003c\/p\u003e \u003cp\u003e17.2 Literature Review 318\u003c\/p\u003e \u003cp\u003e17.3 Methodology 320\u003c\/p\u003e \u003cp\u003e17.4 State of the Art 322\u003c\/p\u003e \u003cp\u003e17.5 Results Analysis 323\u003c\/p\u003e \u003cp\u003e17.5.1 Importing Libraries 323\u003c\/p\u003e \u003cp\u003e17.5.2 Datasets 323\u003c\/p\u003e \u003cp\u003e17.5.3 Model Comparison 324\u003c\/p\u003e \u003cp\u003e17.6 Conclusion 325\u003c\/p\u003e \u003cp\u003eAcknowledgment 326\u003c\/p\u003e \u003cp\u003eReferences 326\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Encryption and Decryption of Credit Card Data Using Quantum Cryptography 331\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSumit Ranjan, Armaan Munshi, Devansh Gupta, Sandeep Kumar Satapathy, Shruti Mishra, Abishi Chowdhury, Sachi Nandan Mohanty and Mannava Yesu Babu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 332\u003c\/p\u003e \u003cp\u003e18.1.1 Evolution of Cryptography: A Historical Perspective 332\u003c\/p\u003e \u003cp\u003e18.1.2 Quantum Cryptography: Unveiling the Quantum Revolution 333\u003c\/p\u003e \u003cp\u003e18.1.3 Quantum Key Distribution Protocols and Practical Implementation 333\u003c\/p\u003e \u003cp\u003e18.1.4 Encryption with Quantum Cryptography 333\u003c\/p\u003e \u003cp\u003e18.1.5 Decryption with Quantum Cryptography 334\u003c\/p\u003e \u003cp\u003e18.1.6 Challenges and Future Prospects 335\u003c\/p\u003e \u003cp\u003e18.2 Related Works 335\u003c\/p\u003e \u003cp\u003e18.3 Methodology 336\u003c\/p\u003e \u003cp\u003e18.3.1 Quantum Key Distribution (QKD) Setup 336\u003c\/p\u003e \u003cp\u003e18.3.2 Key Generation and Distribution 337\u003c\/p\u003e \u003cp\u003e18.3.3 Encryption 337\u003c\/p\u003e \u003cp\u003e18.3.4 Transmission 337\u003c\/p\u003e \u003cp\u003e18.3.5 Decryption 337\u003c\/p\u003e \u003cp\u003e18.3.6 Aes 338\u003c\/p\u003e \u003cp\u003e18.4 Proposed Model 339\u003c\/p\u003e \u003cp\u003e18.4.1 Key Generation 339\u003c\/p\u003e \u003cp\u003e18.4.2 Encryption 340\u003c\/p\u003e \u003cp\u003e18.4.3 Decryption 341\u003c\/p\u003e \u003cp\u003e18.5 Experimental Result\/Result Analysis 341\u003c\/p\u003e \u003cp\u003e18.5.1 Flow Diagram of Quantum Cryptography Encryption and Decryption 341\u003c\/p\u003e \u003cp\u003e18.5.2 Algorithm of the Code 343\u003c\/p\u003e \u003cp\u003e18.6 Conclusion and Future Work 345\u003c\/p\u003e \u003cp\u003eReferences 346\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Securing Secrets: Exploring Diverse Encryption and Decryption Through Cryptography with Deep Dive to AES 349\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYarradoddi Sai Sreenath Reddy, Gurram Thanmai, Kammila Charan Sri Sai Varma, Shruti Mishra, Sandeep Kumar Satapathy, Abishi Chowdhury, Sachi Nandan Mohanty and Mannava Yesu Babu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 350\u003c\/p\u003e \u003cp\u003e19.2 Related Work 353\u003c\/p\u003e \u003cp\u003e19.3 Methodology 357\u003c\/p\u003e \u003cp\u003e19.4 UML Diagram 359\u003c\/p\u003e \u003cp\u003e19.5 Architecture Diagram 360\u003c\/p\u003e \u003cp\u003e19.6 Implementation 360\u003c\/p\u003e \u003cp\u003e19.7 Conclusion 361\u003c\/p\u003e \u003cp\u003eReferences 362\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Secure Pass: Hash-Based Password Generator and Checker with Randomized Function 365\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAneesh Rathore, Ganesh Choudhary, Mradul Goyal, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 366\u003c\/p\u003e \u003cp\u003e20.2 Related Work 368\u003c\/p\u003e \u003cp\u003e20.3 Methodology 370\u003c\/p\u003e \u003cp\u003e20.4 Conclusion and Future Work 376\u003c\/p\u003e \u003cp\u003eReferences 377\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Beyond Passwords: Face Authentication as a Futuristic Solution for Web Security 379\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eParas Yadav, Manya Bhardwaj, Akshita Bhamidimarri, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 380\u003c\/p\u003e \u003cp\u003e21.1.1 Problem Statement 380\u003c\/p\u003e \u003cp\u003e21.1.2 Research Goals 381\u003c\/p\u003e \u003cp\u003e21.2 Literature Review 382\u003c\/p\u003e \u003cp\u003e21.3 Methodology 386\u003c\/p\u003e \u003cp\u003e21.3.1 Face Recognition Algorithms and Techniques 387\u003c\/p\u003e \u003cp\u003e21.3.2 Data Collection and Pre-Processing 387\u003c\/p\u003e \u003cp\u003e21.3.3 Integration with Web Server Architecture 388\u003c\/p\u003e \u003cp\u003e21.4 Proposed Model 389\u003c\/p\u003e \u003cp\u003e21.5 Experimental Result\/Result Analysis 394\u003c\/p\u003e \u003cp\u003e21.5.1 Evaluation and Results 394\u003c\/p\u003e \u003cp\u003e21.5.1.1 Performance Metrics for Face Authentication 394\u003c\/p\u003e \u003cp\u003e21.5.1.2 Comparative Analysis Utilizing Password-Based Systems 395\u003c\/p\u003e \u003cp\u003e21.5.1.3 Evaluation of Usability and User Experience 395\u003c\/p\u003e \u003cp\u003e21.5.2 Security and Privacy Considerations 395\u003c\/p\u003e \u003cp\u003e21.5.2.1 Implementing Measures to Safeguard Biometric Data 395\u003c\/p\u003e \u003cp\u003e21.5.2.2 Vulnerability Analysis and Countermeasures 396\u003c\/p\u003e \u003cp\u003e21.5.2.3 Legal and Ethical Considerations 396\u003c\/p\u003e \u003cp\u003e21.6 Conclusion and Future Work 396\u003c\/p\u003e \u003cp\u003e21.6.1 Contributions and Resulting Effects 397\u003c\/p\u003e \u003cp\u003e21.6.2 Areas for Future Research Exploration 397\u003c\/p\u003e \u003cp\u003e21.6.3 Implementation Recommendations 397\u003c\/p\u003e \u003cp\u003eReferences 398\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Cryptographic Key Application for Biometric Implementation in Automobiles 401\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePriyansh Chatap, Kavish Paul, Akshat Gupta, Sandeep Kumar Satapathy, Sung-Bae Cho, Shruti Mishra, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 402\u003c\/p\u003e \u003cp\u003e22.2 Related Work 405\u003c\/p\u003e \u003cp\u003e22.3 Methodology 407\u003c\/p\u003e \u003cp\u003e22.4 Proposed Methodology 409\u003c\/p\u003e \u003cp\u003e22.5 Results and Analysis 414\u003c\/p\u003e \u003cp\u003e22.6 Conclusion 415\u003c\/p\u003e \u003cp\u003eReferences 417\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Password Strength Testing: An Overview and Evaluation 419\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTanmay Agrawal, Kaushal Kanna, Azeem, Abishi Chowdhury, Shruti Mishra, Sandeep Kumar Satapathy, Janjhyam Venkata Naga Ramesh and Sachi Nandan Mohanty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 420\u003c\/p\u003e \u003cp\u003e23.2 Related Work 421\u003c\/p\u003e \u003cp\u003e23.3 Methodology 422\u003c\/p\u003e \u003cp\u003e23.4 Result 425\u003c\/p\u003e \u003cp\u003e23.5 Discussion 426\u003c\/p\u003e \u003cp\u003e23.6 Conclusion 427\u003c\/p\u003e \u003cp\u003e23.7 Future Work 428\u003c\/p\u003e \u003cp\u003eReferences 429\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Digital Forensics Analysis on the Internet of Things and Assessment of Cyberattacks 431\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSaswati Chatterjee, Suneeta Satpathy and Pratik Kumar Swain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 432\u003c\/p\u003e \u003cp\u003e24.2 Background 433\u003c\/p\u003e \u003cp\u003e24.2.1 Relevant Work 434\u003c\/p\u003e \u003cp\u003e24.2.2 Cyber Kill Chain 434\u003c\/p\u003e \u003cp\u003e24.2.3 SANS Artifacts Categorization 435\u003c\/p\u003e \u003cp\u003e24.3 The D4I Framework 436\u003c\/p\u003e \u003cp\u003e24.3.1 Mapping and Categorization of Digital Artifacts 436\u003c\/p\u003e \u003cp\u003e24.3.2 A Way to Explain in Detail How to Examine and Analyze 437\u003c\/p\u003e \u003cp\u003e24.4 Application Illustration 438\u003c\/p\u003e \u003cp\u003e24.4.1 Integrating the D4I Framework with IoT Forensics 439\u003c\/p\u003e \u003cp\u003e24.5 Discussion 440\u003c\/p\u003e \u003cp\u003e24.6 Conclusion 441\u003c\/p\u003e \u003cp\u003eReferences 442\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Closing the Security Gap: Towards Robust and Explainable AI for Diabetic Retinopathy 445\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eR. S. M. Lakshmi Patibandla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 446\u003c\/p\u003e \u003cp\u003e25.2 Security Challenges in AI-Based DR Diagnosis 450\u003c\/p\u003e \u003cp\u003e25.2.1 Data Poisoning 450\u003c\/p\u003e \u003cp\u003e25.2.2 Adversarial Attacks 451\u003c\/p\u003e \u003cp\u003e25.2.3 Privacy Violations 452\u003c\/p\u003e \u003cp\u003e25.3 Building Robust and Explainable AI Systems 453\u003c\/p\u003e \u003cp\u003e25.3.1 Robust Model Design and Training 453\u003c\/p\u003e \u003cp\u003e25.3.2 Data Augmentation to Enhance Model Generalizability 454\u003c\/p\u003e \u003cp\u003e25.3.3 Interpretable Deep Learning and Explainable AI 456\u003c\/p\u003e \u003cp\u003e25.3.4 Demystifying Deep Learning Predictions 458\u003c\/p\u003e \u003cp\u003e25.3.5 Strict Data Governance and Privacy-Preserving Techniques 459\u003c\/p\u003e \u003cp\u003e25.3.6 Performance of Strong Data Security Protocols 461\u003c\/p\u003e \u003cp\u003e25.4 Benefits of Robust and Explainable AI 464\u003c\/p\u003e \u003cp\u003e25.5 Conclusion: The Future of Secure AI in DR Diagnosis 468\u003c\/p\u003e \u003cp\u003eReferences 468\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Applications of Leveraging Diverse Machine Learning Models for Heart Stroke Prediction and its Security Aspects in Healthcare 473\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBusa Shannu Sri, Kotha Dinesh Sai and U. M. Gopal Krishna\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 474\u003c\/p\u003e \u003cp\u003e26.2 Literature Review 474\u003c\/p\u003e \u003cp\u003e26.3 Approaches 475\u003c\/p\u003e \u003cp\u003e26.4 Analysis and Interpretation 477\u003c\/p\u003e \u003cp\u003e26.5 Machine Learning and Security Considerations 480\u003c\/p\u003e \u003cp\u003e26.6 Suggestions 480\u003c\/p\u003e \u003cp\u003e26.7 Conclusion 481\u003c\/p\u003e \u003cp\u003eReferences 482\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Enhancing Healthcare Security: A Revolutionary Methodology for Deep Learning-Based Intrusion Detection 483\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eM. Priyachitra, Prasanjit Singh, D. Senthil and Ellakkiya Sekar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 484\u003c\/p\u003e \u003cp\u003e27.2 Allied Works 486\u003c\/p\u003e \u003cp\u003e27.3 Proposed IDS Approach 488\u003c\/p\u003e \u003cp\u003e27.3.1 Data Collection 489\u003c\/p\u003e \u003cp\u003e27.3.2 Data Preprocessing 489\u003c\/p\u003e \u003cp\u003e27.3.3 Feature Extraction 490\u003c\/p\u003e \u003cp\u003e27.3.4 Intrusion Detection Using GRU 490\u003c\/p\u003e \u003cp\u003e27.3.4.1 Gated Recurrent Unit 490\u003c\/p\u003e \u003cp\u003e27.3.4.2 Optimization of GRU Using ACO Algorithm 492\u003c\/p\u003e \u003cp\u003e27.4 Results and Discussion 493\u003c\/p\u003e \u003cp\u003e27.4.1 Dataset Description 493\u003c\/p\u003e \u003cp\u003e27.4.2 Performance Evaluation 493\u003c\/p\u003e \u003cp\u003e27.4.3 Comparative Analysis 496\u003c\/p\u003e \u003cp\u003e27.5 Conclusion 497\u003c\/p\u003e \u003cp\u003eReferences 497\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 AI and ML Application in Cybersecurity Hazard Recognition: Challenges, Opportunities, and Future Perspectives in Ethiopia, Horn of Africa 501\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShashi Kant and Metasebia Adula\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction 502\u003c\/p\u003e \u003cp\u003e28.2 AI and ML Application in Cybersecurity Hazard Recognition 504\u003c\/p\u003e \u003cp\u003e28.3 Detailed Applications of AI and ML in Ethiopia Perspectives 505\u003c\/p\u003e \u003cp\u003e28.3.1 Variance Recognition in Ethiopia 505\u003c\/p\u003e \u003cp\u003e28.3.1.1 Probable Challenges in Implementing AI and ML for Variance Recognition in Ethiopia 507\u003c\/p\u003e \u003cp\u003e28.3.1.2 Opportunities in Implementing AI and ML Opportunities for Variance Recognition in Ethiopia 508\u003c\/p\u003e \u003cp\u003e28.3.2 Intrusion Recognition and Princidenceion Softwares (IDPS) for Hazard Recognition in Ethiopia 510\u003c\/p\u003e \u003cp\u003e28.3.2.1 Challenges That Arise When Learning AI and ML-Grounded IDPS Software’s in Ethiopia 511\u003c\/p\u003e \u003cp\u003e28.3.2.2 Opportunities in Implementation of AI and ML-Grounded IDPS Software’s in Ethiopia 513\u003c\/p\u003e \u003cp\u003e28.3.3 Browser Hijacking Software Recognition in Ethiopia 514\u003c\/p\u003e \u003cp\u003e28.3.3.1 Challenges in Browser Hijacking Software Recognition in Ethiopia 516\u003c\/p\u003e \u003cp\u003e28.3.3.2 Solutions for Browser Hijacking Software Recognition Challenge in Ethiopia 517\u003c\/p\u003e \u003cp\u003e28.4 Scam and Deception Recognition in Ethiopia 518\u003c\/p\u003e \u003cp\u003e28.4.1 Challenges in Scam and Deception Recognition in Ethiopia 519\u003c\/p\u003e \u003cp\u003e28.4.2 Opportunities of AI and ML Application in Scam and Deception Recognition in Ethiopia 520\u003c\/p\u003e \u003cp\u003e28.5 Hazard Acumen Examination in Ethiopia 522\u003c\/p\u003e \u003cp\u003e28.5.1 Challenges in Hazard Acumen Examination in Ethiopia 523\u003c\/p\u003e \u003cp\u003e28.5.2 AI and ML application in Hazard Acumen Examination in Ethiopia 524\u003c\/p\u003e \u003cp\u003e28.6 AI and ML in Cybersecurity: Future Perspectives in Ethiopia 525\u003c\/p\u003e \u003cp\u003e28.6.1 Future Perspectives 526\u003c\/p\u003e \u003cp\u003e28.7 Conclusion 526\u003c\/p\u003e \u003cp\u003eAcknowledgement 527\u003c\/p\u003e \u003cp\u003eReferences 528\u003c\/p\u003e \u003cp\u003eIndex 531\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433524687128,"sku":"9781394305223","price":171.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394305223.jpg?v=1784853433","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/protecting-and-mitigating-against-cyber-threats-deploying-artificial-intelligence-and-machine-learning-hardback-9781394305223","provider":"Freshly Printed Books","version":"1.0","type":"link"}