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Malware Data Science: Attack Detection and Attribution
出版社 No Starch
ISBN 9781593278595
分類 Computer & Information Technology > Programming
價格 HK$475.00
 
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Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization.

Security has become a "big data" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist.

In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis.

You'll learn how to:
- Analyze malware using static analysis
- Observe malware behavior using dynamic analysis
- Identify adversary groups through shared code analysis
- Catch 0-day vulnerabilities by building your own machine learning detector
- Measure malware detector accuracy
- Identify malware campaigns, trends, and relationships through data visualization

Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.


About the Authors:

Joshua Saxe is Chief Data Scientist at major security vendor, Sophos, where he leads a security data science research team. He's also a principal inventor of Sophos' neural network-based malware detector, which defends tens of millions of Sophos customers from malware infections. Before joining Sophos, Joshua spent 5 years leading DARPA funded security data research projects for the US government.

Hillary Sanders leads the infrastructure data science team at Sophos, which develops the frameworks used to build Sophos' deep learning models. Before joining Sophos, Hillary created a recipe web app and spent three years as a data scientist at Premise Data Corporation.


Introduction

Chapter 1: Basic Static Malware Analysis (NOW AVAILABLE!)

Chapter 2: Beyond Basic Static Analysis: x86 Disassembly (NOW AVAILABLE!)

Chapter 3: A Brief Introduction to Dynamic Analysis (NOW AVAILABLE!)

Chapter 4: Identifying Adversary Campaigns Through Malware Relationship Analysis (NOW AVAILABLE!)

Chapter 5: Identifying Adversary Groups Through Share Code Analysis (NOW AVAILABLE!)

Chapter 6: Catching 0-day by Building Your Own Machine Learning Malware Detector (NOW AVAILABLE!)

Chapter 7: Building a Machine Learning-Based Detector in Python

Chapter 8: Measuring Malware Detector Accuracy

Chapter 9: Identifying Malware Campaigns, Trends, and Relationships Through Visualization

Chapter 10: The Basics of Deep Learning

Chapter 11: Using keras to Implement a Neural Network

Chapter 12: Conclusion

Appendix A: Documentation of Tools Accompanying Book

Appendix B: Malware Dataset Descriptions


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