SEC595: SANS Munich February 2022

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What You Will Learn

SEC595 is a crash-course introduction to practical data science, statistics, probability, and machine learning. The course is structured as a series of short discussions with extensive hands-on labs that help students develop a solid and intuitive understanding of how these concepts relate and can be used to solve real-world problems. If you've never done anything with data science or machine learning but want to use these techniques, this is definitely the course for you!

Unlike other courses in this space, SEC595 is squarely centered on solving information security problems. Where other courses tend to be at the extremes of teaching almost all theory or solving trivial problems that don't translate to the real world, this course strikes a balance. We cover only the theory and math fundamentals that you absolutely must know, and only in so far as they apply to the techniques that we then put into practice.

The major topics covered in SEC595 include:
  • Data acquisition from SQL, NoSQL document stores, web scraping, and other common sources
  • Data exploration and visualization
  • Descriptive statistics
  • Inferential statistics and probability
  • Bayesian inference
  • Unsupervised learning and clustering
  • Deep learning neural networks
You Will Be Able To:
  • Apply statistical models to real-world problems in meaningful ways
  • Generate visualizations of your data
  • Perform mathematics-based threat hunting on your network
  • Understand and apply unsupervised learning/clustering methods
  • Build Deep Learning Neural Networks
  • Build and understand Convolutional Neural Networks
  • Understand and build Genetic Search Algorithms
You Will Receive with this Course:
  • A supporting virtual machine
  • Jupyter notebooks of all of the labs and complete solutions
This Course Will Prepare You To:
  • Build AI anomaly detection tools
  • Model information security problems in useful ways
  • Build useful visualization dashboards
  • Solve problems with neural networks
Additional Resources:
  • Anaconda
  • TensorFlow (and supporting libraries)
  • Matplotlib
  • VMWare Workstation/Player/Fusion

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Start date Location / delivery
14 Feb 2022 Munich Book now

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