Course Overview
Traditional cybersecurity methods include creating barriers around your infrastructure to protect it from intruders. However, as enterprises continue to digitally transform, they’re faced with a proliferation of devices, more sophisticated cybersecurity attacks, and an incredibly vast network of data to protect—which means new cybersecurity methodologies must be explored. An alternative approach is to address cybersecurity as a data science problem: Aim to better understand all the users and activities across your network so that you can identify which transactions are typical and which are potentially nefarious.
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
Prerequisites
- Familiarity with defensive cybersecurity themes
- Professional data science and/or data analysis experience
- Competency with the Python programming language
- Competency with the Linux command line
Course Objectives
- Build Morpheus pipelines to process and perform AI-based inference on massive amounts of data for cybersecurity use cases in real time
- Utilize several AI models with a variety of data input types for tasks like sensitive information detection, anomalous behavior profiling, and digital fingerprinting
- Leverage key components of the Morpheus AI framework, including the Morpheus SDK and command-line interface (CLI), and NVIDIA Triton™ Inference Server