data_hacking
A collection of IPython notebooks demonstrating data analysis and machine learning techniques on security datasets.
What is data_hacking?
Data Hacking is a collection of IPython notebooks that demonstrate data analysis and machine learning techniques applied to cybersecurity datasets. It provides practical examples of using Python's data science stack to solve security problems like malware detection, network traffic analysis, and file classification. The project serves as an educational resource for security professionals learning data science techniques.
Security analysts, threat researchers, and cybersecurity professionals who want to apply data science and machine learning techniques to security data. Also valuable for data scientists interested in cybersecurity applications.
It offers realistic, hands-on examples with actual security datasets, showing both successful approaches and common pitfalls. Unlike theoretical tutorials, it demonstrates practical applications of data science tools to real security problems.
Overview
Data Hacking Project
Use Cases
Best For
- Learning how to apply Pandas and Scikit-learn to security data analysis
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