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.
