Book description
"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."
Table of contents
- Cover Page
- Title Page
- Copyright Page
- Dedication
- About the Authors
- About the Technical Reviewer
- BRIEF CONTENTS
- CONTENTS IN DETAIL
- FOREWORD by Anup Ghosh
- ACKNOWLEDGMENTS
- INTRODUCTION
- 1 BASIC STATIC MALWARE ANALYSIS
- 2 BEYOND BASIC STATIC ANALYSIS: X86 DISASSEMBLY
- 3 A BRIEF INTRODUCTION TO DYNAMIC ANALYSIS
- 4 IDENTIFYING ATTACK CAMPAIGNS USING MALWARE NETWORKS
-
5 SHARED CODE ANALYSIS
- Preparing Samples for Comparison by Extracting Features
- Using the Jaccard Index to Quantify Similarity
- Using Similarity Matrices to Evaluate Malware Shared Code Estimation Methods
- Building a Similarity Graph
- Scaling Similarity Comparisons
- Building a Persistent Malware Similarity Search System
- Running the Similarity Search System
- Summary
- 6 UNDERSTANDING MACHINE LEARNING–BASED MALWARE DETECTORS
- 7 EVALUATING MALWARE DETECTION SYSTEMS
- 8 BUILDING MACHINE LEARNING DETECTORS
- 9 VISUALIZING MALWARE TRENDS
- 10 DEEP LEARNING BASICS
- 11 BUILDING A NEURAL NETWORK MALWARE DETECTOR WITH KERAS
- 12 BECOMING A DATA SCIENTIST
- APPENDIX AN OVERVIEW OF DATASETS AND TOOLS
- Index
Product information
- Title: Malware Data Science
- Author(s):
- Release date: September 2018
- Publisher(s): No Starch Press
- ISBN: 9781593278595
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