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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

Transforming a malware binary into a topological space

Now, let’s expand on how topological analysis of malware begins with transforming the malware binary or behavioral data into a topological space, often a simplicial complex. Topological analysis of malware is a modern approach to cybersecurity that involves converting complex data about malware into a form that can be better understood and studied. This process might sound abstract and complicated, but let’s break it down into more relatable terms using an analogy.

Imagine that you’re looking at a massive crowd of people from a bird’s-eye view. Each person in this crowd can be thought of as a data point. Now, suppose you want to understand more about the people in the crowd – their relationships, groups, and any unusual behavior. Trying to examine each person individually would be an overwhelming task. A much more efficient approach would be to look for patterns within the crowd.

This is...

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