I’m very pleased to announce that this year, my team and I got two classes accepted for the BlackHat conference in Las Vegas! I believe that data science and machine learning have a huge role to play in infosec/cybersecurity, and in a way, it really is a domain which is crying out for data science to be used. There are ever expanding amounts of data , the actors are becoming more sophisticated, and the security professionals are almost always strained for resources. Our classes won’t turn you into data scientists, but you will learn how to directly apply data science techniques to cybersecurity. If this sounds interesting to you, please check out our Crash Course in Data Science and our Crash Course in Machine Learning. Both are two day classes and will be offered from July 30-31st and Aug 1-2, 2016.
This interactive course will teach network security professionals how to use data science techniques to quickly write scripts to manipulate and analyze network data. Students will learn techniques to rapidly write scripts to improve their work. Participants will learn now to read in data in a variety of common formats then write scripts to analyze and visualize that data. A non-exhaustive list of what will be covered include:
- How to write scripts to read CSV, XML, and JSON files
- How to quickly parse log files and extract artifacts from them
- How to make API calls to merge datasets
- How to use the Pandas library to quickly manipulate tabular data
- How to effectively visualize data using Python
- How to apply simple machine learning algorithms to identify potential threats
Finally, we will introduce the students to cutting edge Big Data tools including Apache Spark and Apache Drill, and demonstrate how to apply these techniques to extremely large datasets.
This interactive course will teach network security professionals machine learning techniques and applications for network data. This course is a continuation of the skills taught in the Crash Course in Data Science for Hackers. Students will learn various machine learning methods, applications, model selection, testing, and interpretation. Participants will write code to prepare and explore their data and then apply machine learning methods for discovery.
A non-exhaustive list of what will be covered include:
- Machine Learning Introduction and Terminology
- Foundations of Statistics
- Python Machine Learning Packages Introduction
- Data Exploration and Presentation
- Supervised Learning Methods
- Unsupervised Learning Methods
- Model Selection and Testing
- Machine Learning Applications for Network Data