Cyber Data Science is the art of combining techniques from machine learning and statistics to solve cyber security tasks. Today machine learning algorithms take a substantiation part in any well-known security product such as Intrusion detection and prevention systems, Security orchestration, Behavioral authentications, Endpoint detection, Data leakage prevention, Vulnerability prioritization and much more.
This site is designed to create common knowledge between cyber data scientists and to enable everyone to become one.
Data Science + Cyber Security
A data scientist is a combination of a programmer, statistician, and a domain expert. This combination of skills, knowledge, and expertise allows the data scientist to use Artificial Intelligence techniques (and mostly machine learning) to solve problems in his domain.
The Cyber Data Scientist’s goal is to solve problems from the cybersecurity domain using machine learning algorithms.
The Fundamental keys of the Cyber Data Scientist:
- Cyber domain expertise: understand how cyberspace works, what the cyber-attack lifecycle is, and how cyber/information security measures treat them.
- Statistics, AI & ML knowledge: Master statistic and machine learning in order to find the most suitable way to model the problem
- Practice: code and deploy the solution to real-world problems and datasets.
If you came to this blog, you are probably missing one of these three fundamentals. Identify the chapters that you need to improve and focus on them when you are reading the blog.
Important links?
- Learn cybersecurity
- Learn statistics and machine learning
- Learn data science workflow
- Start a project with datasets
- Practice and learn more in our courses