SEC595: Applied Data Science and AI/Machine Learning for Cybersecurity Professionals 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. 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 • 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 Author Statement “AI and machine learning are everywhere. How do the vendor solutions work? Is this really black magic? I wrote this course to fill an enormous knowledge gap in our field. I believe that if you are going to use a tool, you should understand how that tool works. If you don’t, you don’t really know what the results mean or why you are getting them. This course provides you a crash-course in statistics, mathematics, Python, and machine learning, taking you from zero to...I’m reluctant to promise ‘Hero...’ Let’s say competent who-can-solve-real-problems-today person!” —David Hoelzer 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 • Build AI anomaly detection tools • Model information security problems in useful ways • Build useful visualization dashboards • Solve problems with Neural Networks 6 Day Program 36 CPEs Laptop Required sans.org/sec595 • Watch a preview of this course • Discover how to take this course: Online, In-Person GMLE Machine Learning Engineer giac.org/gmle GIAC Machine Learning Engineer The GIAC Machine Learning Engineer (GMLE) certification validates a practitioner’s knowledge of practical data science, statistics, probability, and machine learning. GMLE certification holders have demonstrated that they are qualified to solve real-world cyber security problems using Machine Learning. • Anomaly detection and optimization • Convolutional neural networks • Data acquisition • Data exploration and visualization • Data manipulation and analysis • Deep learning neural networks • Inferential statistics and probability • Loss functions • Probability and inference • Python scripting • Supervised and unsupervised learning GMLE Machine Learning Engineer giac.org/gmle
