Detection Engineering Masterclass: Part 2
Published 7/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.89 GB | Duration: 5h 28m

Detection Engineering Zero to Hero

[b]What you'll learn[/b]

Understand how to write detection documentation

Ability to automate document validation

Learn GitHub actions to validate documents automatically

Write Python scripts to sync up the detection library with the SIEM

Write Python scripts to create metrics

[b]Requirements[/b]

Completion of "Detection Engineering Masterclass: Part 1"

Basic understanding of Python

[b]Description[/b]

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 overetection 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!

Overview

Section 1: TOML

Lecture 1 TOML Overview

Lecture 2 Setting up a Development Environment

Lecture 3 Reviewing Elastic Rule TOML

Lecture 4 Working with the Elastic Detection Rules Repo

Lecture 5 Validating TOML Syntax Using Taplo

Lecture 6 Creating an Elastic TOML Template

Lecture 7 Enforcing TOML Required Fields

Lecture 8 Working with Multiple TOML Files

Lecture 9 Creating a MITRE Object in Python

Lecture 10 Validating MITRE Data in our TOML - Part 1

Lecture 11 Validating MITRE Data in our TOML - Part 2

Lecture 12 Converting and Validating our Detections

Section 2: Elastic API

Lecture 13 Introduction

Lecture 14 Obtaining your API Key

Lecture 15 Pushing a Sample Rule

Lecture 16 Writing a TOML to JSON Script

Lecture 17 GET'ing Our First Rule and Managing Rule IDs

Lecture 18 Working our Custom Detections

Lecture 19 Updating our Custom Detections

Section 3: GitHub

Lecture 20 Overview

Lecture 21 GitHub Actions Introduction

Lecture 22 Uploading our Detections and Code

Lecture 23 Creating our TOML Validation Action

Lecture 24 Enforcing Validation Checks

Lecture 25 Syncing with Elastic - Part 1

Lecture 26 Syncing with Elastic - Part 2

Section 4: Metrics

Lecture 27 Overview

Lecture 28 Converting our TOML to CSV

Lecture 29 Converting our TOML to MD

Lecture 30 Converting our TOML to ATT&CK Navigator JSON

Lecture 31 Creating our Metrics GitHub Action

Lecture 32 Creating Status Badges

Section 5: Conclusion

Lecture 33 Conclusion

security analysts,incident responders,detection engineers,cyber security college students