AI Tactical Skills: IOT Hacking & Defense

• Cyber Security engineers/analysts
• Network and system administrators
• Drone, & Robotic Engineers & Developers
• Drone Operators
• Digital Forensics Investigators
• Penetration Testers
• Cloud computing personnel
• Cloud project managers
• Operations support looking for career advancement

• Understand the fundamentals of IoT and AI
• Set up and configure development boards for AI-enabled IoT projects
• Develop and deploy AI models for various IoT applications
• Build and integrate IoT systems for smart homes, industrial applications, and smart cities
• Analyze and visualize data from IoT devices using AI and cloud platforms
• Implement a comprehensive AI-enabled IoT solution as a capstone project

Each participant will get 6 months access to Premier Private Lab-Range

Module 1: Introduction to AI and IoT

• Basics of IoT / Artificial Intelligence
• Introduction to AI concepts and its importance in IoT
• Overview of Machine Learning (ML) and Deep Learning (DL)
• Key AI frameworks and tools for IoT (TensorFlow, PyTorch, OpenCV)

Module 2: Setting Up the Development Environment

• Introduction to IoT Development Platforms
• AI for IoT hardware device options
• IoT Communication Protocols
• Detailed look at MQTT, HTTP, CoAP, and other protocols
• Setting up a basic MQTT server
• Connecting sensors and actuators to the development board

Module 3: Handling Data

• Delta Lake and Databricks
• Data collection
• Garbage data = no ML
• Streaming data into IoT Hub
• Z-spike anomaly detection

Module 4: Machine Learning for IoT

• IoT sensors with anomaly detection
• Regression with IoMT
• Classifying sensor with decision trees
• Deep learning predictive maintenance
• Face detection
• Z-spike anomaly detection

Module 5: Deep Learning

• Analyzing traffic patterns using AI
• Keras fall detection
• LSTM to predict device failure
• Deploying models

Module 6: AI Anomaly Techniques for IoT

• Z-Spikes using sense HAT on Rpi
• Use of autoencoders in labeled data
• Isolated Forest
• Anomalies on the edge

Module 7: Cloud Integration and Data Analytics

• Integrating IoT with Cloud Platforms
• Overview of cloud platforms (AWS IoT, Azure IoT, Google Cloud IoT)
• Connecting IoT devices to the cloud

Module 8: Computer Vision

• OpenCV camera deployment
• Deep neural nets and Caffe
• Object detection with NVIDIA Jetson Nano
• PyTorch on GPU's

Module 9: NLP (natural language processing)

• Speech to text
• Luis (language understanding with Microsoft)
• Deploying smart bots
• Enhancing bots with QnA

Module 10: Optimization of MCU

• ESP32 for IoT in Azure
• Streaming machine learning with Kafka and Spark
• Enriching data with Kafka

Module 11: Deploying to the edge

• OTA updates
• Offloading to the web with Tensorflow.js
• Mobile model
• Distributed machine learning using Fog computing