Malware Data Science: Attack Detection and Attribution

Malware Data Science: Attack Detection and Attribution

<b><i>Malware Data Science</i> explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization.</b><br><br>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.<br> <br>In <i>Malware Data Science</i>, 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. <br> <br>You'll learn how to:<br>- Analyze malware using static analysis<br>- Observe malware behavior using dynamic analysis<br>- Identify adversary groups through shared code analysis<br>- Catch 0-day vulnerabilities by building your own machine learning detector<br>- Measure malware detector accuracy<br>- Identify malware campaigns, trends, and relationships through data visualization<br> <br>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, <i>Malware Data Science</i> will help you stay ahead of the curve.