Malware Data Science

Malware Data Science

Attack Detection and Attribution
by Joshua Saxe with Hillary Sanders
August 2018, 400 pp.

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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.

Author Bio 

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.

Table of contents 

Now Available in Early Access!

Chapter 1: Basic Static Malware Analysis
Chapter 2: Beyond Basic Static Analysis: x86 Disassembly
Chapter 3: A Brief Introduction to Dynamic Analysis
Chapter 4: Identifying Adversary Campaigns Through Malware Relationship Analysis
Chapter 5: Identifying Adversary Groups Through Share Code Analysis
Chapter 6: Catching 0-day by Building Your Own Machine Learning Malware Detector
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​