RCCE Course
Course #211

Hands-On Detection engineering: Blueprint

📊 Level: Beginner
⏱️ Duration: 2 Days
🏷️ Track: SOC
📋 Prerequisites: None
🖥️ Mode: Online Instructor-Led
📝 Course Description

RCCE students will learn how to build, test, and maintain high-fidelity detection rules across SIEM, EDR, and cloud security platforms. RCCE students will learn to translate threat intelligence and MITRE ATT&CK techniques into detection logic, write detection rules using query languages (SPL, KQL, Sigma), reduce false positive rates through rule tuning, implement detection-as-code workflows, version control detection content, measure detection coverage gaps, and build automated testing pipelines that validate detection rules against simulated attack data before production deployment. This practice-intensive course emphasizes applied skills through lab exercises, real-world scenarios, and production-realistic workflows. Starting from foundational concepts, RCCE students will learn by doing, building muscle memory and practical confidence through repeated hands-on engagement. Students complete exercises that mirror actual workplace tasks, ensuring skills transfer directly to their professional roles.

🎯 Target Audience
  • SOC Analysts and Incident Responders
  • Detection Engineers and SIEM Content Authors
  • Threat Hunters improving adversary coverage
  • Security Operations Team Leads
  • Professionals implementing Hands-On Detection engineering: Blueprint
🧠 What You Will Learn
  • Build detections and response workflows for privilege escalation
  • Explain Course Overview fundamentals
  • Execute hands-on tasks for what you will learn
  • Build detections and response workflows for privilege escalation, including Translate threat intel to, and Write rules in SPL, KQL,.
  • Execute hands-on tasks for applied engineering — covering Reduce false positives.
  • Execute hands-on tasks for operational maturity — covering detection coverage.
  • Execute hands-on tasks for course approach — covering Practice-intensive with lab exercises and real-world scenarios, Skills transfer directly to professional SOC roles.
  • Execute hands-on tasks for why it matters
  • Execute hands-on tasks for key outcomes — covering Systematic creation of, Reduces mean time to detect.
📚 Course Outline
Module 01Hands-On Detection Engineering
Module 02Build, Test, and Maintain High-Fidelity Detection Rules
Module 03Course Overview
Module 04What You Will Learn
Module 05Detection Fundamentals
Module 06Applied Engineering
Module 07Operational Maturity
Module 08Course Approach
Module 09What Is Detection Engineering?
Module 10Why It Matters
Module 11Key Outcomes
Module 12Detection Engineering Lifecycle
Module 13Plan & Research
Module 14Build & Test
🧪 Lab Details

All hands-on labs run on Rocheston Rose X OS. Students practice hands-on detection engineering: blueprint by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.

  • Lab 1: Build detections and response workflows for privilege escalation
  • Lab 2: Build detections and response workflows for privilege escalation
  • Lab 3: Explain Course Overview fundamentals
  • Lab 4: Execute hands-on tasks for what you will learn
  • Lab 5: Build detections and response workflows for privilege escalation
📊 Skill Level
Beginner
Beginner Intermediate Advanced Expert
Duration
2 Days
🎓
Certificate
Completion
🖥️
Lab Platform
Rose X OS
👨‍🏫
Mode of Training
Online Instructor-Led
🔥
Platform
Zelfire
🐦‍⬛
Cyber Range
Raven
📓
Study Material
CyberNotes
🏆 Certificate

Upon successful completion of this course, students will receive an official RCCE Course Completion Certificate for Hands-On Detection engineering: Blueprint, verifiable through the Rocheston certification portal.

🔑 Student Access & Materials
  • Full access to all course materials and slide decks
  • Hands-on lab access on Rocheston Rose X OS environment
  • Access to Rocheston CyberNotes
  • Access to Rocheston Zelfire — EDR/XDR SIEM platform
  • Access to Rocheston Raven — online cyber range exercise platform
  • Access to Rocheston Vulnerability Vines AI