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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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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

Leveraging classification to manage threat response

The domain of cybersecurity presents a unique challenge, characterized by a continual need to adapt to evolving threats. Each malware sample represents an ongoing effort by malicious actors to subvert digital systems. Understanding these threats at a deeper level can be the key to crafting effective defenses and neutralizing them. This is where TDA comes into play, offering an advanced methodology to classify and comprehend these threats.

In the context of malware analysis, classification is more than just about assigning labels to unknown samples. It’s about understanding the fundamental nature of the threat. This is where TDA, and particularly persistent homology, can offer profound insights. When we classify malware using persistent homology, we’re not simply assigning it into a category based on a shallow comparison of signatures. Instead, we’re delving deeper, examining the topological shape of the data...

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