Part 1. Android Malware Analysis -- Chapter 1. A Deep Vision-based Multi-Class Classification System of Android Malware Apps -- Chapter 2. Android Malware detection based on network analysis and federated learning -- Chapter 3. ASParseV3: Auto Static Parser & Customizable Visualizer -- Part 2. Network Malware Analysis -- Chapter 4. Fast Flux Service Networks: Architecture, Characteristics and Detection Mechanisms -- Chapter 5. Efficient Graph-based Malware Detection using Minimized Kernel and SVM -- Chapter. 6 Deep Learning for Windows Malware Analysis -- Part 3. IoT Malware Analysis -- Chapter 7. Malware analysis for IoT and Smart AI-based Applications -- Chapter 8. A Multi-Class Classification Approach for IoT Intrusion Detection Based on Feature Selection and Oversampling -- Chapter 9. Malware Mitigation in Cloud Computing Architecture.

This book provides the foundational aspects of malware attack vectors and appropriate defense mechanisms against malware. The book equips readers with the necessary knowledge and techniques to successfully lower the risk against emergent malware attacks. Topics cover protections against malware using machine learning algorithms, Blockchain and AI technologies, smart AI-based applications, automated detection-based AI tools, forensics tools, and much more. The authors discuss theoretical, technical, and practical issues related to cyber malware attacks and defense, making it ideal reading material for students, researchers, and developers. Presents theoretical, technical, and practical knowledge on defending against malware attacks; Covers malware applications using machine learning algorithms, Blockchain and AI, forensics tools, and much more; Includes perspectives from experts in cybersecurity at different institutions, including academia, research centers, and companies.