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

Product type Paperback

Published in Dec 2023

Publisher Packt

ISBN-13 9781804618646

Length 230 pages

Edition 1st Edition

Concepts

Threat Hunting

Author (1):

Shane Molinari

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Table of Contents (15) Chapters

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

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Appendix

AI techniques used in malware data analysis

AI techniques used in malware data analysis, such as machine learning and deep learning, have revolutionized malware data analysis. These techniques enable the development of models capable of automatically learning patterns and features from large datasets, facilitating the detection and classification of malware samples. By leveraging AI, security analysts can automate the analysis process, reduce human error, and enhance the overall efficiency and accuracy of malware detection.

Machine learning techniques are widely applied in malware detection and analysis. Machine learning models learn patterns and features from large datasets to identify and classify malware samples. Key machine learning techniques that are utilized in malware analysis include supervised learning, unsupervised learning, and deep learning.