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.
Author(s): Iman Almomani; Leandros A. Maglaras; Mohamed Amine Ferrag; Nick Ayres
Series: Security Informatics and Law Enforcement
Publisher: Springer International Publishing
Year: 2023
Language: English
Pages: 310
Cover
Front Matter
1. A Deep-Vision-Based Multi-class Classification System of Android Malware Apps
2. Android Malware Detection Based on Network Analysis and Federated Learning
3. ASParseV3: Auto-Static Parser and Customizable Visualizer
4. Fast-Flux Service Networks: Architecture, Characteristics, and Detection Mechanisms
5. Efficient Graph-Based Malware Detection Using Minimized Kernel and SVM
6. Deep Learning for Windows Malware Analysis
7. Malware Analysis for IoT and Smart AI-Based Applications
8. A Multiclass Classification Approach for IoT Intrusion Detection Based on Feature Selection and Oversampling
9. Malware Mitigation in Cloud Computing Architecture
Back Matter
