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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 (14) 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. Other Books You May Enjoy Appendix: Index

Topological Data Analysis for Malware Detection and Analysis

The advent of internet technologies has ushered in unprecedented opportunities for global communication and the exchange of information. However, it has also introduced a plethora of security threats, notably malware. These malicious software are designed to infiltrate, damage, or disrupt computing systems, often with severe consequences. Traditional malware detection methods have had varying levels of success but have also highlighted the need for more sophisticated approaches. This chapter explores the application of Topological Data Analysis (TDA) in malware detection and analysis, underscoring its potential to enhance cybersecurity measures.

Applying TDA to malware analysis presents a novel, efficient, and robust technique to identify and categorize malware. Unlike conventional analysis methods, which often hinge on known malware signatures or heuristic rules, TDA does not require prior knowledge of the data. Instead...

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