Malware Data Science Attack Detection and Attribution 1st edition by Joshua Saxe, Hillary Sanders 9781593278601 1593278608

Malware Data Science Attack Detection and Attribution 1st edition by  Joshua Saxe, Hillary Sanders – Ebook PDF Instant Download/Delivery.9781593278601, 1593278608
Full download Malware Data Science Attack Detection and Attribution 1st edition  after payment

Product details:

ISBN 10: 1593278608
ISBN 13: 9781593278601 
Author: Joshua Saxe; Hillary Sanders

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.

Malware Data Science Attack Detection and Attribution 1st Table of contents:

1: BASIC STATIC MALWARE ANALYSIS
The Microsoft Windows Portable Executable Format
Dissecting the PE Format Using pefile
Examining Malware Images
Examining Malware Strings
Summary

2: BEYOND BASIC STATIC ANALYSIS: X86 DISASSEMBLY
Disassembly Methods
Basics of x86 Assembly Language
Disassembling ircbot.exe Using pefile and capstone
Factors That Limit Static Analysis
Summary

3: A BRIEF INTRODUCTION TO DYNAMIC ANALYSIS
Why Use Dynamic Analysis?
Dynamic Analysis for Malware Data Science
Basic Tools for Dynamic Analysis
Limitations of Basic Dynamic Analysis
Summary

4: IDENTIFYING ATTACK CAMPAIGNS USING MALWARE NETWORKS
Nodes and Edges
Bipartite Networks
Visualizing Malware Networks
Building Networks with NetworkX
Adding Nodes and Edges
Network Visualization with GraphViz
Building Malware Networks
Building a Shared Image Relationship Network
Summary

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
Steps for Building a Machine Learning–Based Detector
Understanding Feature Spaces and Decision Boundaries
What Makes Models Good or Bad: Overfitting and Underfitting
Major Types of Machine Learning Algorithms
Summary

7: EVALUATING MALWARE DETECTION SYSTEMS
Four Possible Detection Outcomes
Considering Base Rates in Your Evaluation
Summary

8: BUILDING MACHINE LEARNING DETECTORS
Terminology and Concepts
Building a Toy Decision Tree–Based Detector
Building Real-World Machine Learning Detectors with sklearn
Building an Industrial-Strength Detector
Evaluating Your Detector’s Performance
Next Steps
Summary

9: VISUALIZING MALWARE TRENDS
Why Visualizing Malware Data Is Important
Understanding Our Malware Dataset
Using matplotlib to Visualize Data
Using seaborn to Visualize Data
Summary

10: DEEP LEARNING BASICS
What Is Deep Learning?
How Neural Networks Work
Training Neural Networks
Types of Neural Networks
Summary

11: BUILDING A NEURAL NETWORK MALWARE DETECTOR WITH KERAS
Defining a Model’s Architecture
Compiling the Model
Training the Model
Evaluating the Model
Enhancing the Model Training Process with Callbacks
Summary

12: BECOMING A DATA SCIENTIST
Paths to Becoming a Security Data Scientist
A Day in the Life of a Security Data Scientist
Traits of an Effective Security Data Scientist
Where to Go from Here

APPENDIX: AN OVERVIEW OF DATASETS AND TOOLS
Overview of Datasets
Tool Implementation Guide
Index

People also search for Malware Data Science Attack Detection and Attribution 1st :

malware attack statistics
    
what are the symptoms of malware attack
    
how does malware avoid detection
    
malware data science pdf
    
malware attribution