Download Malware Data Science PDF book free: Attack Detection and Attribution by Joshua Saxe – From Malware Data Science PDF: Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Buy from Amazon
Table of Contents
Malware Data Science PDF
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. Malware Data Science PDF
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.
Details About Malware Data Science PDF by Joshua Saxe
- Name: Malware Data Science: Attack Detection and Attribution
- Authors: Joshua Saxe and Hillary Sanders
- Publish Date: September 25, 2018
- Language: English
- Genre: Software Testing, Computer Viruses, and Network Security
- Format: PDF
- Size: 24Â MB
- Pages: 272
Editorial Reviews
Review – Malware Data Science PDF
“For those looking to become a security data scientist, or just want to get a comprehensive understanding of how to use data science to deal with malicious software, Malware Data Science is a superb reference.”Â
—Ben Rothke, RSA Conference
Review
As a data scientist and machine learning practitioner I was disappointed in this book. Admittedly the authors faced the very difficult task of trying to communicate the relationship of two very deep and technical subjects: malware analysis and machine learning. The result, unfortunately, is a work that is only the barest introduction to both. Malware Data Science PDF
The first half of the book dips a toe in the ocean of malware static analysis, dynamic analysis, and reverse engineering. Not knowing anything about malware, this section of the book did indeed spark my curiosity and supply me with strong motivation to continue my studies in malware analysis with other, more comprehensive resources.
The second half of the book is a shallow overview of basic, elementary topics in machine learning and common tools used by practitioners. This overview is accompanied by proof-of-concept applications to malware detection. The machine learning overview will not supply any new knowledge to anyone who has any practical experience in machine learning and for a complete beginner it is insufficient even as a first course.
In summary, this is not a bad book as the authors are clearly technically proficient, good writers, and passionate about their area of expertise. Nonetheless, be aware that if you are not already an expert in malware AND machine learning you will need to follow up this short read with much more detailed sources. Malware Data Science PDF
If you are already proficient in both subjects than you will already understand how to apply machine learning to malware analysis to the level of basic examples found in this book.
About the Author
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. Malware Data Science PDF
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. You might also like A pratical introduction to numerology
