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via Udemy |
Go to Course: https://www.udemy.com/course/detection-engineering-masterclass-part-1/
The Detection Engineering Masterclass: Part 1 on Coursera is an engaging and comprehensive course designed to introduce learners to the fundamental concepts and practical skills of security detection engineering. Whether you're new to cybersecurity or have some background, this course offers a well-structured pathway from theory to hands-on implementation. **Course Overview and Content:** This two-part series starts with foundational theories behind security operations and detection engineering. It then guides you through building your own home lab environment using VirtualBox and Elastic Security tools. You'll perform three increasingly complex attack scenarios, learning how to generate logs, create detections, and document them effectively. A significant portion of the course is dedicated to scripting in Python, where you'll write validation scripts, interact with Elastic through APIs, and automate detection workflows. The course culminates with deploying your detections on GitHub and integrating with Elastic via GitHub Actions, plus generating metrics and visualizations. **Strengths:** - **Practical, Hands-On Approach:** The course emphasizes real-world skills by setting up a lab environment, executing attack scenarios, and automating detections. - **Progressive Difficulty:** The attack scenarios increase in complexity, allowing learners to steadily build confidence and technical competence. - **Comprehensive Skill Development:** You’ll cover detection engineering theory, scripting, automation, and metrics visualization, giving a holistic view of the detection lifecycle. - **Resource Accessibility:** All course code is available on GitHub, facilitating self-paced learning and practical experimentation. - **Good for Beginners:** While some familiarity with security operations can be helpful, the course is designed to be approachable for newcomers eager to delve into detection engineering. **Requirements:** To get the most out of this course, your system should be capable of running multiple VMs with at least 4 CPU cores and 8GB RAM (preferably more). Basic knowledge of Linux, Windows, or security concepts will ease the learning curve, but there are supportive instructions for newcomers. **Review Summary:** This course is highly recommended for aspiring security analysts, detection engineers, and security architects who want a solid foundation with practical experience. Its blend of theory, lab exercises, scripting, and automation makes it a valuable asset for those aiming to develop a detection engineering skill set from scratch or enhance existing knowledge. **Final Recommendation:** If you're motivated to learn detection engineering and are comfortable with the prerequisites, this course offers excellent value. Its step-by-step guidance, real-world scenarios, and focus on automation prepare learners for a critical role in security operations. Enroll in the Detection Engineering Masterclass: Part 1 to start building your detection architecture expertise today!
Welcome to the Detection Engineering Masterclass: Part 1!Two Part Course OverviewThis course will first teach the theory behind security operations and detection engineering. We'll then start building out our home lab using VirtualBox and Elastic's security offering. Then we'll run through three different attack scenarios, each more complex than the one prior. We'll make detections off of our attacks, and learn how to document our detections. Next we'll dive more into coding and Python by writing validation scripts and learning out to interact with Elastic through their API. Wrapping everything up, we'll host all our detections on GitHub and sync with Elastic through our own GitHub Action automations. As a cherry on top, we'll have a final section on how to write scripts to gather important metrics and visualizations.This course takes students from A-Z on the detection engineering lifecycle and technical implementation of a detection engineering architecture.While this course is marketed as entry level, any prerequisite knowledge will help in the courses learning curve. Familiarity with security operations, searching logs, security analysis, or any related skillset will be helpful (but ultimately not required).Part One OverviewThis is part one of a two part series on Detection Engineering! This course is meant to kickstart anyone interested in security analysis, detection engineering, and security architecture. The first part is the meat of the course, where we will go over:Detection Engineering TheorySetting Up our LabWorking with Logging and our SIEMRunning Attack Scenarios to generate logs and create alertsLearn how to use Atomic Red Team for testingThe second part deals with detection as code philosophies, which will be very Python and GitHub heavy (but don't worry! I'll walk you through everything step by step.)By the end of this two part course, you'll have a full stack detection engineering architecture. You'll be able to:Run offensive testsReview the logsMake alertsSave alerts using a standardized templateEnforce template data through codeProgrammatically push the alerts to the SIEMRun periodic metrics off the detection dataThe entire course runs ~11 or so hours in length, but should take ~20-40 hours to complete fully. All code written will be available on the course GitHub in case you'd like to skip the Python heavy sections.RequirementsThe ability to run 2-3 VMs on a local machine:Ubuntu LinuxParrotOSWindows 11Minimum RequirementsCPU Cores: 4RAM: 8gbHard Drive Space: 50GBRecommended RequirementsCPU Cores: 6+RAM: 16GB+ Hard Drive Space: 50GB+You can technically get by with the main host having only a couple cores and 8 gigs of RAM, but any additional resources that can be assigned to your VMs will make the process smoother.Thanks for stopping by!