SEC595: SANS Secure Japan 2024

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What You Will Learn

Data Science, Artificial Intelligence, and Machine Learning aren't just the current buzzwords, they are fast becoming one of the primary tools in our information security arsenal. The problem is that, unless you have a degree in mathematics or data science, you're likely at the mercy of the vendors. This course completely demystifies machine learning and data science. More than 70% of the time in class is spent solving machine learning and data science problems hands-on rather than just talking about them.

Unlike other courses in this space, this course is squarely centered on solving information security problems. Where other courses tend to be at the extremes, teaching almost all theory or solving trivial problems that don't translate into the real world, this course strikes a balance. While this course will cover necessary mathematics, we cover only the theory and fundamentals you absolutely must know, and only so as to allow you to understand and apply the machine learning tools and techniques effectively. We show you how the math works but don't expect you to do it. The course progressively introduces and applies various statistic, probabilistic, or mathematic tools (in their applied form), allowing you to leave with the ability to use those tools. The hands-on projects covered were selected to provide you a broad base from which to build your own machine learning solutions. If you want or need to know how AI tools like ChatGPT really work so that you can intelligently discuss their potential uses in your organization, in addition to knowing how to build effective solutions to solve real cybersecurity problems using machine learning and AI today, this is the class you need to take. Check out the extensive course description below for a detailed run down of course content and don't miss the free demo available by clicking the "Course Demo" button above!
Major Topics Covered 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
  • Autoencoders
  • Loss functions
  • Convolutional networks
  • Embedding layers
Business Takeaways

This course will help your organization:
  • Generate useful visualization dashboards
  • Solve problems with Neural networks
  • Improve the effectiveness, efficiency, and success of cybersecurity initiatives
  • Build custom machine learning solutions for your organization's specific needs
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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