RCCE Course
Course #421

Hands-On Windows artifacts: Mastery

📊 Level: Advanced
⏱️ Duration: 2 Days
🏷️ Track: DFIR
📋 Prerequisites: DFIR foundations
🖥️ Mode: Online Instructor-Led
📝 Course Description

RCCE students will learn Windows forensic artifacts including registry hives (SAM, SYSTEM, SOFTWARE, NTUSER.DAT), event logs, prefetch files, shimcache, amcache, jump lists, LNK files, and browser artifacts. RCCE students will learn to extract and analyze Windows registry data for evidence of attacker activity, parse Windows event logs for security-relevant events, interpret prefetch data to determine program execution history, analyze shimcache and amcache for evidence of deleted executables, reconstruct user activity from jump lists and recent files, and correlate multiple artifact sources to build comprehensive investigation timelines. This practice-intensive course emphasizes applied skills through lab exercises, real-world scenarios, and production-realistic workflows. At an expert level, 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
  • Security Engineers building defensive controls
  • Security Analysts and Blue Team members
  • Systems Administrators with security responsibilities
  • GRC and Risk Professionals supporting controls
  • Professionals implementing Hands-On Windows artifacts: Mastery
🧠 What You Will Learn
  • Execute hands-on tasks for hands-on windows artifacts: mastery
  • Execute hands-on tasks for knowledge goals
  • Execute hands-on tasks for skills goals — covering Extract and parse registry hives.
  • Execute hands-on tasks for windows forensic artifact landscape
  • Execute hands-on tasks for registry hives
  • Execute hands-on tasks for event logs
  • Execute hands-on tasks for execution artifacts — covering SAM, SYSTEM, SOFTWARE.
  • Execute hands-on tasks for recent docs, shellbags — covering Security, System, Application.
  • Execute hands-on tasks for artifact evidence value matrix
  • Execute hands-on tasks for proves execution
  • Execute hands-on tasks for user attribution
  • Execute hands-on tasks for survives delete
📚 Course Outline
Module 01Hands-On Windows Artifacts: Mastery
Module 02Knowledge Goals
Module 03Skills Goals
Module 04Windows Forensic Artifact Landscape
Module 05Registry Hives
Module 06Event Logs
Module 07Execution Artifacts
Module 08Recent docs, Shellbags
Module 09Artifact Evidence Value Matrix
Module 10Proves Execution
Module 11User Attribution
Module 12Survives Delete
Module 13Registry Hives Architecture
Module 14Windows Registry
🧪 Lab Details

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

  • Lab 1: Execute hands-on tasks for hands-on windows artifacts: mastery
  • Lab 2: Execute hands-on tasks for knowledge goals
  • Lab 3: Execute hands-on tasks for skills goals
  • Lab 4: Execute hands-on tasks for windows forensic artifact landscape
  • Lab 5: Execute hands-on tasks for registry hives
📊 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 Hands-On Windows artifacts: Mastery, 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