IoEd-Net: Internet of Educational Things Dataset for Academic Network Analysis
Catalog Summary
"Comprehensive dataset from University of New Brunswick containing over 202,085 network traffic records from IoT devices in academic environment, with classification of benign and malicious activities."
Catalog Notes
Dataset Overview
The IoEd-Net dataset, collected by the University of New Brunswick (UNB), provides a comprehensive collection of network traffic data from IoT devices in an academic network environment. It contains over 202,085 records covering both benign and malicious activities, making it ideal for cybersecurity research and anomaly detection studies.
Key Features
- 55 core features including network traffic metrics, device behavior, and operational status
- 3 derived features for enhanced analysis of data flows and anomaly detection
- Labeled data for benign and malicious activities
- Geographic information (latitude/longitude) for devices
- Multi-dimensional telemetry data from educational IoT devices
Data Structure/Columns
- Source/Destination Info: IP addresses, network ports, geolocation data
- Traffic Characteristics: Packet sizes, interarrival times, flow duration, bytes sent/received, packet rates
- Device Telemetry: CPU usage, memory consumption, energy usage, device uptime
- Protocol Info: TCP, UDP, ICMP distribution
- Temporal Features: Session durations, packet arrival times
- Labels: Binary classification for benign vs malicious traffic
Data Collection Method
Data was collected from IoT devices in a real academic network environment at the University of New Brunswick, with careful monitoring of network traffic and simulated malicious activities to create a realistic cybersecurity dataset.
Research Applications
- Malware detection in academic IoT networks
- Anomaly detection and cybersecurity research
- IoT traffic analysis for identifying and mitigating network threats
- Development of advanced Intrusion Detection Systems (IDS)
- Network behavior profiling and pattern recognition
Machine Learning Use Cases
- Binary classification (benign vs malicious traffic)
- Time series analysis for temporal patterns
- Deep learning models for threat detection
- Feature engineering from raw network data
- Supervised learning for attack type classification
Data Preview
| Source IP | Dest IP | Source Port | Dest Port | Protocol | Flow Duration | Packets Sent | Bytes Sent | CPU Usage | Memory Usage | Label |
|---|---|---|---|---|---|---|---|---|---|---|
| 192.168.1.10 | 10.0.0.50 | 45678 | 80 | TCP | 1200.5 | 150 | 45678 | 45.2 | 512.3 | Benign |
| 172.16.0.20 | 8.8.8.8 | 53 | 443 | UDP | 850.3 | 89 | 23456 | 78.5 | 1024.7 | Malicious |
| 10.10.10.5 | 192.168.2.100 | 22 | 8080 | TCP | 2450.8 | 320 | 98765 | 32.1 | 256.9 | Benign |
Showing first few rows for preview
Provided Citation
IoEd-Net Dataset, University of New Brunswick (UNB), 2024. Available at: Kaggle.
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Source metadata: Kaggle (2026)
Indexed by IoTDataset.com on Jan 16, 2026
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