Malware Data Science : Attack, Detection, and Attribution

Malware Data Science : Attack, Detection, and Attribution

Details

Author(s)
Joshua Saxe, Hillary Sanders
Format
Paperback | 1 pages
Dimensions
178 x 235 x 17.78mm | 622g
Publication date
21 Dec 2018
Publisher
No Starch Press,US
Publication City/Country
San Francisco, United States
Language
English
Illustrations note
1 Illustrations, unspecified
ISBN10
1593278594
ISBN13
9781593278595
Bestsellers rank
50,030

Description

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.


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