MalwareDataScience
Published at https://y1ngyang.github.io/MalwareDataScience/
For these projects I’m using the book:
ISBN-13: 978-1-59327-859-5
link: https://www.malwaredatascience.com/home
##Table of contents
- Introduction
- Chapter 1: Basic Static Malware Analysis
- Chapter 2: Beyond Basic Static Analysis: x86 Disassembly
- Chapter 3: A Brief Introduction to Dynamic Analysis
- Chapter 4: Identifying Attack Campaigns Using Malware Networks
- Chapter 5: Shared Code Analysis
- Chapter 6: Understanding Machine Learning-Based Malware Detectors
- Chapter 7: Evaluating Malware Detection Systems
- Chapter 8: Building Machine Learning Detectors
- Chapter 9: Visualizing Malware Trends
- Chapter 10: Deep Learning Basics
- Chapter 11: Building a Neural Network Malware Detector with Keras
- Chapter 12: Becoming a Data Scientist
- Appendix: An Overview of Datasets and Tools
Keywords: machine learning, statistics, social network analysis, data visualization, malware detection and analysis methods.
**The aim is to learn how to:
1. Analyze malware using static analysis
2. Observe malware behavior using dynamic analysis
3. Identify adversary groups through shared code analysis
4. Catch 0-day vulnerabilities by building your own machine learning detector
5. Measure malware detector accuracy
6. Identify malware campaigns, trends, and relationships through data visualization
Tools:
- Anaconda
- Jupyter Notebooks
- I’ll be using VirtualBox 5.2.18 or later version for my Ubuntu Virtual Machine as well as the code and data that accompany the book from this link: https://www.malwaredatascience.com/ubuntu-virtual-machine
[link to my website!] (https://www.gespada.com)