Objective
Build a multi-class classification model that can accurately identify the type of IoT device by analyzing the statistical features of its network traffic. This enables automated device inventory management, security policy enforcement, and rogue device detection.
Business Value
For Network Engineers transitioning to ML/AI:- Automated Device Discovery: Automatically identify and catalog IoT devices on the network
- Security Policy Enforcement: Apply device-specific security rules and access controls
- Network Segmentation: Automatically assign devices to appropriate network segments
- Asset Management: Maintain accurate inventory of connected IoT devices
- Anomaly Detection: Identify unauthorized or rogue devices on the network
Real-World Applications:- Smart building device management
- Industrial IoT security and monitoring
- Healthcare device compliance and tracking
- Campus network device identification
- Retail and hospitality IoT management
Core Libraries
- pandas & numpy: Data processing and feature engineering
- LightGBM: High-performance gradient boosting for multi-class classification
- scikit-learn: Data preprocessing and evaluation metrics
- matplotlib & seaborn: Visualization of results and feature importance
Dataset
UNSW-IoT Traffic Profile Dataset (Kaggle)- Source: Kaggle dataset with labeled network traffic from 28 distinct IoT devices
- Size: Large-scale dataset with comprehensive network traffic features
- Features: Statistical network traffic characteristics (ports, timings, protocols)
- Target: Device category classification across multiple IoT device types
Key Features:- TCP/UDP port numbers and protocols
- Packet timing characteristics (tcp.time_delta)
- HTTP content lengths and headers
- Flow duration and packet counts
- Protocol-specific statistical features
Success Criteria
1. Multi-class Accuracy: Achieve >85% overall classification accuracy
2. Per-class Performance: Maintain >80% F1-score for each device category
3. Feature Interpretability: Identify top network characteristics for fingerprinting
4. Scalability: Handle large-scale IoT device deployments efficiently
5. Real-time Capability: Fast inference for network monitoring applications
Key Insights
Technical Learnings:- Network Fingerprinting: IoT devices have unique network traffic signatures
- Feature Engineering: Protocol-specific features most discriminative
- Multi-class Classification: Handle imbalanced device categories effectively
- Gradient Boosting: LightGBM optimal for tabular network data
Network Engineering Applications:- Device Inventory: Automated discovery and cataloging of IoT devices
- Security Policies: Device-type specific access control and monitoring
- Network Planning: Optimize network resources based on device types
- Compliance: Ensure only authorized device types connect to network
Next Steps & Extensions
Immediate Improvements
1. Real-time Integration: Connect to network monitoring tools for live classification
2. New Device Detection: Identify unknown device types not in training data
3. Confidence Scoring: Provide classification confidence for uncertain devices
4. Temporal Analysis: Track device behavior changes over time
Advanced Features
1. Deep Learning: Implement neural networks for complex traffic pattern recognition
2. Federated Learning: Train across multiple network environments
3. Adversarial Detection: Identify devices trying to mimic other device types
4. Multi-modal Learning: Combine network traffic with other device characteristics
Production Deployment
1. Network Controller Integration: Deploy on switches and wireless controllers
2. SIEM Integration: Connect with security monitoring systems
3. API Development: Create device classification APIs for network management
4. Dashboard Creation: Real-time visualization of device classifications
This project demonstrates how machine learning can automate IoT device management, providing network engineers with powerful tools for security, compliance, and operational efficiency in IoT-rich environments.