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Data Science for Malware Analysis

You're reading from   Data Science for Malware Analysis A comprehensive guide to using AI in detection, analysis, and compliance

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781804618646
Length 230 pages
Edition 1st Edition
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Author (1):
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Shane Molinari Shane Molinari
Author Profile Icon Shane Molinari
Shane Molinari
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Toc

Table of Contents (15) Chapters Close

Preface 1. Part 1– Introduction
2. Chapter 1: Malware Science Life Cycle Overview FREE CHAPTER 3. Chapter 2: An Overview of the International History of Cyber Malware Impacts 4. Part 2 – The Current State of Key Malware Science AI Technologies
5. Chapter 3: Topological Data Analysis for Malware Detection and Analysis 6. Chapter 4: Artificial Intelligence for Malware Data Analysis and Detection 7. Chapter 5: Behavior-Based Malware Data Analysis and Detection 8. Part 3 – The Future State of AI’s Use for Malware Science
9. Chapter 6: The Future State of Malware Data Analysis and Detection 10. Chapter 7: The Future State of Key International Compliance Requirements 11. Chapter 8: Epilogue – A Harmonious Overture to the Future of Malware Science and Cybersecurity
12. Index 13. Other Books You May Enjoy Appendix

Homology

Recall that I mentioned TDA’s strength lies in its persistence homology – a tool that identifies and quantifies topological features at various scales. Persistence homology is one of the most powerful tools in the TDA toolbox. To explain it simply, let’s use the analogy of taking photographs of a mountain range at different altitudes.

Imagine you’re in a helicopter, ascending from the base to the peak of a mountain range. As you ascend, you take a series of photographs. At the base, you capture individual mountains. As you rise higher, you start to see groups of mountains and then entire sections of the mountain range. By the time you’re at the peak, you have a complete, bird’s-eye view of the range.

In this analogy, each photograph you take represents a scale. Just like how different scales reveal different details about the mountain range, persistence homology explores data at different scales to uncover various topological...

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