Malware Data Science: Attack, Detection, and Attribution

Author:   Joshua Saxe ,  Hillary Sanders
Publisher:   No Starch Press,US
ISBN:  

9781593278595


Pages:   272
Publication Date:   25 September 2018
Format:   Paperback
Availability:   To order   Availability explained
Stock availability from the supplier is unknown. We will order it for you and ship this item to you once it is received by us.

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Malware Data Science: Attack, Detection, and Attribution


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Overview

Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. 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.

Full Product Details

Author:   Joshua Saxe ,  Hillary Sanders
Publisher:   No Starch Press,US
Imprint:   No Starch Press,US
Weight:   0.622kg
ISBN:  

9781593278595


ISBN 10:   1593278594
Pages:   272
Publication Date:   25 September 2018
Audience:   General/trade ,  General
Format:   Paperback
Publisher's Status:   Active
Availability:   To order   Availability explained
Stock availability from the supplier is unknown. We will order it for you and ship this item to you once it is received by us.

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Reviews

""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.""  —Ben Rothke, RSA Conference ""If you are new to data science or machine learning, this book provides an excellent introduction to these topics."" —DMFR Security


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. --Ben Rothke, RSA Conference


"""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.""  —Ben Rothke, RSA Conference ""If you are new to data science or machine learning, this book provides an excellent introduction to these topics."" —DMFR Security"


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. --Ben Rothke, RSA Conference


Author Information

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.

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