RCCE Course
Course #1056

Dynamic Malware Analysis and Sandboxing

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

RCCE students will learn how to observe malware safely in controlled environments to capture behavior, persistence, network activity, process execution, and anti-analysis logic. RCCE students will learn to build detonation workflows, collect behavioral telemetry, analyze file and registry changes, inspect command-and-control communications, and validate whether a sample matches known malware families or tradecraft patterns. The course covers practical scenarios ranging from sandbox preparation to execution analysis and reporting. RCCE students will learn to analyze complex systems and think like an attacker to better defend the organization. This comprehensive course delivers practical knowledge applicable to real-world cybersecurity operations. Starting from foundational concepts, RCCE students will learn through a combination of concept explanation, practical demonstration, and hands-on exercises.

🎯 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 Dynamic Malware Analysis and Sandboxing
🧠 What You Will Learn
  • Execute hands-on tasks for dynamic malware analysis
  • Explain Course Overview fundamentals
  • Execute hands-on tasks for what you will learn
  • Execute hands-on tasks for course structure — covering Safely observe malware in sandboxes, 5 modules covering analysis pipeline.
  • Execute hands-on tasks for learning objectives
  • Execute hands-on tasks for sandbox mastery
  • Execute hands-on tasks for analysis skills
  • Execute hands-on tasks for operational output — covering isolated analysis VMs, Trace process execution trees, Produce malware behavior.
  • Execute hands-on tasks for static analysis — covering Examine code without execution.
  • Execute hands-on tasks for dynamic analysis — covering Execute sample in controlled environment.
  • Execute hands-on tasks for why dynamic analysis matters
  • Execute hands-on tasks for rapid ioc extraction — covering Real execution defeats packing.
📚 Course Outline
Module 01Dynamic Malware Analysis
Module 02Course Overview
Module 03What You Will Learn
Module 04Course Structure
Module 05Learning Objectives
Module 06Sandbox Mastery
Module 07Analysis Skills
Module 08Operational Output
Module 09Static Analysis
Module 10Dynamic Analysis
Module 11Why Dynamic Analysis Matters
Module 12Rapid IOC extraction
Module 13Sandbox Architecture Overview
Module 14Core Components
🧪 Lab Details

All hands-on labs run on Rocheston Rose X OS. Students practice dynamic malware analysis and sandboxing by implementing the controls discussed in class, with a focus on real-world deployment, monitoring, and validation.

  • Lab 1: Execute hands-on tasks for dynamic malware analysis
  • Lab 2: Explain Course Overview fundamentals
  • Lab 3: Execute hands-on tasks for what you will learn
  • Lab 4: Execute hands-on tasks for course structure
  • Lab 5: Execute hands-on tasks for learning objectives
📊 Skill Level
Intermediate
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 Dynamic Malware Analysis and Sandboxing, 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