Textbook in PDF format 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. Preface Part I: Foundations of AI & ML in Security Foundations of AI and ML in Security Application of AI and ML in Threat Detection Artificial Intelligence and Machine Learning Applications in Threat Detection Part II: AI & ML Applications in Threat Detection Comparison Study Between Different Machine Learning (ML) Models Integrated with a Network Intrusion Detection System (NIDS) Applications of AI, Machine Learning and Deep Learning for Cyber Attack Detection AI-Based Prioritization of Indicators of Intelligence in a Threat Intelligence Sharing Platform Email Spam Classification Using Novel Fusion of Machine Learning and Feed Forward Neural Network Approaches Intrusion Detection in Wireless Networks Using Novel Classification Models Detection and Proactive Prevention of Website Swindling Using Hybrid Machine Learning Model Part III: Advanced Security Solutions & Case Studies Securing the Future Networks: Blockchain-Based Threat Detection for Advanced Cyber Security Mitigating Pollution Attacks in Network Coding-Enabled Mobile Small Cells for Enhanced 5G Services in Rural Areas Enhancing Multi-Access Edge Computing Efficiency through Communal Network Selection Enhancing Cyber-Security and Network Security Through Advanced Video Data Summarization Techniques Deepfake Face Detection Using Deep Convolutional Neural Networks: A Comparative Study Detecting Low-Rate DDoS Attacks for CS 2 Image Privacy Using Reversible Data Hiding and Encryption Object Detection in Aerial Imagery Using Object Centric Masked Image Modeling (OCMIM) Encryption and Decryption of Credit Card Data Using Quantum Cryptography Securing Secrets: Exploring Diverse Encryption and Decryption Through Cryptography with Deep Dive to AES Secure Pass: Hash-Based Password Generator and Checker with Randomized Function Beyond Passwords: Face Authentication as a Futuristic Solution for Web Security Cryptographic Key Application for Biometric Implementation in Automobiles Password Strength Testing: An Overview and Evaluation Digital Forensics Analysis on the Internet of Things and Assessment of Cyberattacks Closing the Security Gap: Towards Robust and Explainable AI for Diabetic Retinopathy Applications of Leveraging Diverse Machine Learning Models for Heart Stroke Prediction and its Security Aspects in Healthcare Enhancing Healthcare Security: A Revolutionary Methodology for Deep Learning-Based Intrusion Detection AI and ML Application in Cybersecurity Hazard Recognition: Challenges, Opportunities, and Future Perspectives in Ethiopia, Horn of Africa