Malware Science A Comprehensive Guide to Detection, Analysis, and Compliance
Unlock the secrets of malware data science with cutting-edge techniques, AI-driven analysis, and international compliance standards to stay ahead of the ever-evolving cyber threat landscape Key Features Get introduced to three primary AI tactics used in malware and detection Leverage data science to...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing Ltd
[2023]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009790336106719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Dedication
- Foreword
- What the experts say
- Contributors
- Table of Contents
- Preface
- Part 1- Introduction
- Chapter 1: Malware Science Life Cycle Overview
- Combining malware
- Worms and Trojans combination
- Ransomware and spyware combination
- Macro malware and ransomware
- Managing malware
- Collection
- Analysis
- Detection
- Prevention
- Mitigation
- Reporting
- Summary
- Chapter 2: An Overview of the International History of Cyber Malware Impacts
- The evolution of cyber threats and malware
- Impacts on international relations and security
- Impacts on the economy and cybercrime
- The future of malware
- Expanded viewpoint on the impacts on international relations and security
- Expansion on cybercrime impacts on the general economy
- Direct financial impacts of malware - a global overview
- Ransomware's global economic impact - a continental overview
- Ransomware's economic impact in North America - a deeper look
- Ransomware's economic impact in Asia - a detailed examination
- Ransomware's economic impact in Africa - an in-depth analysis
- Ransomware's economic impact in South America - an extensive exploration
- Economic impacts versus socio-economic impacts
- Ransomware attacks and their impact on employment - an in-depth perspective
- Ransomware attacks and their impact on public services - an elaborate examination
- Ransomware and inequality - a closer look at the impact on small businesses
- Policy, regulations, and their downstream impact on smaller businesses and public services
- Regulatory changes due to malware impacts on small and mid-scale businesses
- A deeper dive into the operational challenges
- Translating operational challenges into increased cost
- Expansion of economic and socio-economic impacts on key industries globally.
- Key downstream impacts on key industries globally
- The use of AI systems with malware
- Cybersecurity, malware, and the socio-economic fabric
- Summary
- Part 2 - The Current State of Key Malware Science AI Technologies
- Chapter 3: Topological Data Analysis for Malware Detection and Analysis
- The mathematics of space and continuous transformations
- A deeper dive into the "shape of the data"
- How TDA creates a multi-dimensional data representation
- Transforming a malware binary into a topological space
- Homology
- Persistence homology distinguishes meaningful patterns from random data fluctuations
- Improving detection algorithms to predict the behavior of new malware
- TDA - comparing and contrasting the persistence diagrams of different software
- Using malware persistence diagrams to classify unknown software
- Persistence homology - filtering noise to find meaningful patterns
- Classifying unknown malware with characteristic persistent features
- Leveraging classification to manage threat response
- A deeper dive - employing TDA for threat management
- Summary
- Chapter 4: Artificial Intelligence for Malware Data Analysis and Detection
- AI techniques used in malware data analysis
- Supervised learning
- Challenges and considerations
- Unsupervised learning
- Deep learning for malware analysis deep learning
- Benefits of AI techniques in malware data analysis
- Challenges in AI-based malware analysis
- Benefits of AI in malware detection
- Enhanced detection accuracy
- Future prospects
- Improved adversarial defense
- Hybrid approaches
- Explainable AI (XAI)
- Summary
- Chapter 5: Behavior-Based Malware Data Analysis and Detection
- Behavior-based malware data analysis
- Data collection
- Behavior analysis
- Behavior-based malware detection.
- The concept of proactive behavior-based malware detection
- The concept of malware's behavioral characteristic
- Operational aspects of software behavior data collection
- Operational aspects of behavior modeling using machine learning or AI
- Operational aspects of behavior monitoring
- Operational aspects of malware behavior-based response
- Operational aspects of anomaly detection
- Operational aspects of specification-based techniques
- Normalcy and anomaly detection
- Concept of normalcy
- Concept of anomaly detection
- Operational aspects of anomaly detection
- Future concepts of normalcy and anomaly detection
- Overcoming the increased complexity of evolving cyber threats
- Handling increased complexity and data volume
- Navigating privacy regulations
- Mitigating evolving cyber threats
- Implementing the solutions
- Starting with the basics - organizational capability maturity
- The relationship between the CMMI maturity process and the increased complexity of threat management
- Operational challenges and mitigation strategies to enhance organizational cybersecurity capabilities
- Summary
- Part 3 - The Future State of AI's Use for Malware Science
- Chapter 6: The Future State of Malware Data Analysis and Detection
- The future state of advanced ML and AI integration in malware detection
- Beyond signature-based detection
- The future state of automated malware analysis
- Why manual processes are no longer viable
- The dawn of automated malware analysis
- The future state of cloud-based TI
- The current landscape of TIPs
- The advent of cloud-driven TI
- The future state of integration of big data analytics in cybersecurity
- Understanding the magnitude of modern data
- The imperative for big data analytics
- Integration of AI - the game-changer.
- The future state of deeper OS-level integrations in malware detection
- The current state of malware detection
- The rationale behind OS-level integrations
- Potential avenues for deeper OS-level integrations
- Benefits of deeper OS-level integrations
- Challenges and considerations
- The future state of post-quantum cryptography in countering quantum-vulnerable malware
- Understanding the quantum threat
- Post-quantum cryptography - the new frontier
- Integration in malware detection and defense
- Challenges ahead
- The future state
- The future state of proactive defense mechanisms in cybersecurity
- Why proactive defense?
- The cornerstones of proactive defense
- Advantages of a proactive stance
- Challenges in implementation
- The road ahead - a dynamic defense ecosystem
- The future state of enhanced sandbox environments in cybersecurity
- Modern challenges - evolving malware tactics
- The vision - next-generation sandboxes
- Summary
- Chapter 7: The Future State of Key International Compliance Requirements
- The future state of global data privacy regulations
- The future state of AI ethics and governance standards
- The future state of cybersecurity and risk management
- The future state of supply chain transparency
- The future state of financial crime prevention
- The future state of cross-border data flow regulations
- The future state of climate change regulations
- The future state of blockchain and digital identity
- The future state of RegTech
- The future state of geopolitical dynamics
- Summary
- Chapter 8: Epilogue - A Harmonious Overture to the Future of Malware Science and Cybersecurity
- Appendix
- Index
- About Packt
- Other Books You May Enjoy.
