Cyber malware : offensive and defensive systems /

Cyber malware : offensive and defensive systems / Iman Almomani, Leandros A. Maglaras, Mohamed Amine Ferrag, Nick Ayres, editors.

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 usin...

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Bibliographic Details
Online Access: Full Text (via Springer)
Main Authors: Almomani, Iman (Author), Maglaras, Leandros (Author), Ferrag, Mohamed Amine, 1987- (Author), Ayres, Nick (Author)
Format: eBook
Language:English
Published: Cham : Springer, [2024]
Series:Security informatics and law enforcement.
Subjects:
Malware (Computer software) > Prevention.
Computer security.
Table of Contents:
  • Intro
  • Preface
  • Introduction: Emerging Trends in Cyber-Malware
  • Malware Analysis Techniques
  • Common Types of Cyber-Malware
  • Dynamic and Static Analysis
  • Malware Debugging Techniques
  • Identifying Malware Behavior
  • Malware Distribution Methods
  • Malware Prevention and Mitigation Strategies
  • Future of Cyber-Malware
  • Trends and Predictions for Future Malware Development
  • Emerging Threats and Attack Vectors
  • The Role of Artificial Intelligence in Malware Development and Detection
  • Conclusions and Future Work
  • References
  • Contents
  • 1 A Deep-Vision-Based Multi-class Classification System of Android Malware Apps
  • 1.1 Introduction
  • 1.2 Related Works
  • 1.3 Proposed Deep-Vision-Based Multi-class Classification System
  • 1.4 Evaluations and Discussions
  • 1.4.1 Datasets Description
  • 1.4.2 Security Detection Metrics
  • 1.4.3 Results Analysis
  • 1.5 Conclusions and Future Work
  • References
  • 2 Android Malware Detection Based on Network Analysis and Federated Learning
  • 2.1 Introduction
  • 2.2 Related Studies
  • 2.3 Methodology
  • 2.3.1 Federated Learning Paradigm
  • 2.3.2 Our Proposed Detection Methodology
  • Dataset Processing
  • FDL-Based Model Training
  • 2.4 Result and Discussion
  • 2.5 Conclusion
  • References
  • 3 ASParseV3: Auto-Static Parser and Customizable Visualizer
  • 3.1 Introduction
  • 3.2 Related Works
  • 3.3 Proposed System
  • 3.3.1 System Overview
  • 3.3.2 Features and User Interfaces
  • 3.3.2.1 Uploading Files Window
  • 3.3.2.2 Selecting File Types Window
  • 3.3.2.3 Selecting Keywords Window
  • 3.3.2.4 Scanning Window
  • 3.3.2.5 Visualizing Results and Dashboard Window
  • 3.3.3 Use Case
  • 3.3.3.1 Data Collection
  • 3.3.3.2 Tests and Results
  • 3.3.3.3 Validation
  • 3.4 Conclusion and Future Work
  • References
  • 4 Fast-Flux Service Networks: Architecture, Characteristics, and Detection Mechanisms
  • 4.1 Introduction
  • 4.2 Fast-Flux Service Networks
  • 4.3 Characteristics of Fast-Flux Service Networks
  • 4.3.1 Fast-Flux Domain Names Versus CDN-Hosted Domain Names
  • 4.3.2 Main Characteristics of Fast-Flux Service Networks
  • 4.4 FFSNs Feature Set Collection
  • 4.4.1 Domain Name System-Based Features
  • 4.4.2 IP Geolocation-Based Features
  • 4.4.3 Internet-Wide Scanning-Based Features
  • 4.4.4 Active Delay Measurement-Based Features
  • 4.5 Fast-Flux Detection
  • 4.6 Conclusion
  • References
  • 5 Efficient Graph-Based Malware Detection Using Minimized Kernel and SVM
  • 5.1 Introduction
  • 5.2 Related Work
  • 5.3 API Call Graph-Based Analysis Framework
  • 5.3.1 Extraction of API Call Graph
  • 5.3.2 Extraction of Abstract API Call Graph
  • 5.3.3 Calculation and Reduction of a Graph Kernel
  • 5.3.4 Classification
  • 5.4 Experiments and Testing
  • 5.4.1 Dataset
  • 5.4.2 Evaluation of Kernel Effectiveness
  • 5.4.2.1 Unweighted API Call Graph
  • 5.4.2.2 Weighted API Call Graph
  • 5.4.2.3 Benign-Malware Kernel Results

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