RCCE Course
Course #96

IoT Deep Dive

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

RCCE students will learn Internet of Things security covering smart device vulnerabilities, IoT protocol analysis (MQTT, CoAP, Zigbee, Z-Wave), firmware security assessment, IoT network segmentation, and cloud-connected device risk management. RCCE students will learn to assess IoT device security posture, identify common IoT vulnerabilities including default credentials, insecure update mechanisms, unencrypted communications, and insufficient access controls. The course covers IoT-specific threat modeling, secure IoT deployment architectures, monitoring IoT device behavior for anomalies, and responding to incidents involving compromised IoT devices in enterprise and industrial environments. This deep-dive course provides comprehensive technical coverage that goes beyond surface-level understanding. Building on core knowledge, RCCE students will learn to master the nuances, edge cases, and advanced configurations that separate competent practitioners from true experts. Students will engage with complex real-world scenarios and gain the depth of knowledge required to troubleshoot difficult situations, mentor junior team members, and make architectural decisions with confidence.

🎯 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 IoT Deep Dive
🧠 What You Will Learn
  • Execute hands-on tasks for iot deep dive
  • Execute hands-on tasks for advanced cyber defense mastery
  • Explain Executive Overview fundamentals
  • Execute hands-on tasks for business impact
  • Execute hands-on tasks for defense priorities — covering Operational disruption from compromised, Inventory and classify all IoT assets.
  • Design a scalable privilege management architecture with policy and enforcement
  • Execute hands-on tasks for application layer
  • Execute hands-on tasks for platform/cloud layer
  • Execute hands-on tasks for network layer
  • Execute hands-on tasks for perception/device layer
  • Execute hands-on tasks for smart device vulnerabilities
📚 Course Outline
Module 01IoT Deep Dive
Module 02Advanced Cyber Defense Mastery
Module 03Executive Overview
Module 04Business Impact
Module 05Defense Priorities
Module 06IoT Architecture & Ecosystem
Module 07Four-Layer Reference Architecture for Security Assessment
Module 08Application Layer
Module 09Platform/Cloud Layer
Module 10Network Layer
Module 11Perception/Device Layer
Module 12Smart Device Vulnerabilities
Module 13Hardcoded Credentials
Module 14Insecure Boot
🧪 Lab Details

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

  • Lab 1: Execute hands-on tasks for iot deep dive
  • Lab 2: Execute hands-on tasks for advanced cyber defense mastery
  • Lab 3: Explain Executive Overview fundamentals
  • Lab 4: Execute hands-on tasks for business impact
  • Lab 5: Execute hands-on tasks for defense priorities
📊 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 IoT Deep Dive, 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