RCCE Course
Course #276

Detection engineering Troubleshooting

📊 Level: Advanced
⏱️ Duration: 2 Days
🏷️ Track: SOC
📋 Prerequisites: Foundations
🖥️ 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 diagnostic course focuses on identifying, analyzing, and resolving common failures, misconfigurations, and operational issues. At an expert level, RCCE students will learn systematic troubleshooting methodologies that accelerate root-cause analysis and minimize downtime. Students work through realistic break-fix scenarios that build the diagnostic confidence needed for high-pressure production environments.

🎯 Target Audience
  • SOC Analysts and Incident Responders
  • Detection Engineers and SIEM Content Authors
  • Threat Hunters improving adversary coverage
  • Security Operations Team Leads
  • Professionals implementing Detection engineering Troubleshooting
🧠 What You Will Learn
  • Build detections and response workflows for privilege escalation
  • Explain Course Overview fundamentals
  • Execute hands-on tasks for automation & devops — covering Detection-as-code workflows.
  • Execute hands-on tasks for operational excellence — covering detection coverage gaps.
  • Build detections and response workflows for privilege escalation, including Discipline of designing detection logic, and Hypothesis > Author > Test > Deploy > Tune.
  • Execute hands-on tasks for reduces mean time to detect (mttd) — covering Hypothesis > Author > Test > Deploy > Tune.
  • Execute hands-on tasks for signal vs. noise — covering Low fidelity: broad behavioral triggers.
  • Execute hands-on tasks for threat intel
📚 Course Outline
Module 01Detection Engineering
Module 02Build, Test, Debug & Maintain High-Fidelity Detection Rules
Module 03Course Overview
Module 04Automation & DevOps
Module 05Operational Excellence
Module 06Detection Engineering Fundamentals
Module 07What Is Detection Engineering?
Module 08Detection Lifecycle
Module 09Reduces mean time to detect (MTTD)
Module 10Signal vs. Noise
Module 11Detection Engineering Lifecycle
Module 12Threat Intel
Module 13Feedback Loop
Module 14SIEM Architecture & Data Pipelines
🧪 Lab Details

All hands-on labs run on Rocheston Rose X OS. Students practice detection engineering troubleshooting 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 automation & devops
  • Lab 5: Execute hands-on tasks for operational excellence
📊 Skill Level
Advanced
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 Detection engineering Troubleshooting, 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