Detection Engineering Masterclass: Part 2

Udemy MOOC / Non-credit USD 49.99
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Detection Engineering Masterclass: Part 2

About this course

Welcome to the Detection Engineering Masterclass: Part 2!Don't Purchase if you haven't gone through 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 Two OverviewThis is part two 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 detecti

What you'll learn

  • understand detection engineering theory
  • set up a security lab using VirtualBox and Elastic
  • run and analyze attack scenarios
  • write validation scripts in Python
  • use Elastic's API
  • document detections and host on GitHub

Course objectives

  • to provide a comprehensive understanding of the detection engineering lifecycle
  • to enable students to implement practical detection engineering architectures

Skills you'll gain

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