Black Hat Spring TrainingApplied Data Science and Machine Learning for Cybersecurity
This interactive course will teach security professionals how to use data science techniques to quickly manipulate and analyze network and security data and ultimately uncover valuable insights from this data. The course will cover the entire data science process from data preparation, feature engineering and selection, exploratory data analysis, data visualization, machine learning, model evaluation and optimization and finally, implementing at scale—all with a focus on security-related problems.
By the end of the course, students will be able to:
- Use the python data science ecosystem to rapidly prepare, explore and visualize cybersecurity data
- Build and evaluate common machine learning models and apply these techniques to cybersecurity use cases
- Develop unsupervised models to uncover anomalies and other patterns in their cybersecurity data.
WHO SHOULD TAKE THIS COURSE
Anyone who wishes to incorporate automated data analysis, machine learning and data science into their cybersecurity work. Particularly those working in the following job roles:
- Security Analyst
- SOC Analysts
- SOC Engineers
- CND Analysts
- Security Monitoring
- System Administrators
- Cyber Threat Investigators
- Individuals working on a network hunt team
AUDIENCE SKILL LEVEL
This is a hands-on course. To get the most out of the class and labs, students should be comfortable coding in Python as well as understand common security and network concepts.