Almomani I. Cyber Malware. Offensive and Defensive Systems 2024

Textbook in PDF format

The threat landscape is changing very quickly. With billions of connected IoT devices, mostly reactive detection and mitigation strategies, and fnally big data challenges, we face an extremely rapidly expanding attack surface with a variety of attack vectors, a clear asymmetry between attackers and defenders, and a rapidly expanding attack surface. Additional arguments suggest that cybersecurity approaches must be rethought in terms of reducing the attack surface, making the attack surface dynamic, automating detection, risk assessment, and mitigation, and investigating the prediction and prevention of malware attacks with the use of emerging technologies like blockchain, artifcial intelligence, and machine learning. Additionally, there is a clear asymmetry of attacks and an enormous amount of data.
This book provides the foundational aspects of malware attack vectors and appropriate defense mechanisms against malware. In addition, the book equips you with the necessary knowledge and techniques to successfully lower risk against emergent malware attacks. The book discusses both theoretical, technical, and practical issues related to malware attacks and defense making it an ideal reading material.
Introduction: Emerging Trends in Cyber-Malware
A Deep-Vision-Based Multi-class Classifcation System of Android Malware Apps
Android Malware Detection Based on Network Analysis and Federated Learning
ASParseV3: Auto-Static Parser and Customizable Visualizer
Fast-Flux Service Networks: Architecture, Characteristics, and Detection Mechanisms
Effcient Graph-Based Malware Detection Using Minimized Kernel and SVM
Deep Learning for Windows Malware Analysis
Malware Analysis for IoT and Smart AI-Based Applications
A Multiclass Classifcation Approach for IoT Intrusion Detection Based on Feature Selection and Oversampling
Malware Mitigation in Cloud Computing Architecture