Detection Engineering Masterclass: Part 2

🧠 Dive into Detection Engineering Masterclass: Part 2 🌟

Welcome, Future Security Hero!

🚀 Part One Recap: Before we embark on this thrilling journey, remember to complete the first part of our masterclass. It's the foundation upon which we build your Detection Engineering superpowers. Don't skip this step; it's crucial for understanding what lies ahead!

Two Part Course Overview 📚:

• Theory to Practice: We kick off by covering the fundamentals of security operations and detection engineering theory. Then, we get hands-on, setting up our home lab with VirtualBox and Elastic's security suite.
• Attack Scenarios: You'll be put through three progressively complex attack simulations. Your mission: detect these attacks and document your findings.
• Coding & Python: As we delve deeper, you'll learn to write validation scripts in Python and interact with Elastic through their API. It's all about making detections come to life!
• Automation: We'll show you how to host your detections on GitHub and set up automated processes using GitHub Actions to sync with Elastic.
• Metrics & Visualizations: To top it off, we'll explore writing scripts for important security metrics and creating visualizations to aid decision-making.

Part Two Breakdown 🔍: This is the continuation of a transformative learning experience. In Part Two, we focus on:

• Detection as Code: Embrace the philosophy of detection engineering with a strong emphasis on Python and GitHub. Don't worry; I'll guide you through every step!
• Full Stack Architecture: By the end, you'll have a fully operational detection engineering architecture.

What You'll Learn 🎓:

• Run Offensive Tests: Test your environment for vulnerabilities.
• Review Logs: Analyze logs to identify suspicious activities.
• Make Alerts: Create meaningful alerts that won't drown you in noise.
• Save Alerts: Use standardized templates to document alerts efficiently.
• Enforce Data with Code: Ensure your alerts are consistent and adhere to standards.
• Automate Alert Push: Learn how to programmatically add alerts to your SIEM.
• Metrics Analysis: Run periodic metrics on your detection data for insights.

Course Duration & Effort ⏱️:

• Instruction Time: Approximately 11 hours of video content.
• Completion Time: Expect to spend between 20-40 hours for a complete understanding and implementation. All Python code used will be available on the course GitHub repository for your convenience.

Technical Requirements 🖥️:

• Minimum Hardware: A machine with at least 4 CPU cores, 8GB of RAM, and 50GB of hard drive space. You can get by with these specs, but performance will improve with more resources, especially if you're running multiple VMs.
• Recommended Hardware: Aim for a host with 6+ CPU cores, 16GB+ of RAM, and 50GB+ of hard drive space for an optimal experience.

Join Us on This Adventure in Detection Engineering!

Are you ready to unlock the secrets of detection engineering and take your security analysis skills to the next level? Enroll in "Detection Engineering Masterclass: Part 2" today and embark on a learning journey that will transform how you think about cybersecurity. Let's turn those theoretical concepts into practical, real-world skills! 🛡️✨