DALHOUSIE NIMS LAB IOT 2024 DATASET

Citation Author(s):
Jeffrey
Adjei
Dalhousie University
Nur
Zincir-Heywood
Dalhousie University
Biswajit
Nandy
Solana Networks
Nabil
Seddigh
Solana Networks
Submitted by:
Jeffrey Adjei
Last updated:
Sat, 06/08/2024 - 13:01
DOI:
10.21227/shzy-z570
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Abstract 

This dataset presents real-world IoT device traffic captured under a scenario termed "Active," reflecting typical usage patterns encountered by everyday users. Our methodology emphasizes the collection of authentic data, employing rigorous testing and system evaluations to ensure fidelity to real-world conditions while minimizing noise and irrelevant capture.

The dataset comprises of nine popular IoT devices namely
Amcrest Camera
Smarter Coffeemaker
Ring Doorbell
Amazon Echodot
Google Nestcam
Google Nestmini
Kasa Powerstrip
Samsung 32 inch Smart Television (TV)
Amazon Smartplug

Each device's traffic is stored in individual .pcap files. For our research, we extract flows from these .pcap files using flow analysis tools precisely (Tranalyzer and NFStream). The dataset is organized into device-specific folders, with each containing the "Active" scenario and corresponding .pcap files labeled as active.iteration.pcap within it.

Comprehensive details regarding our setup and methodology are provided in our paper, along with a thorough explanation of the dataset's structure in the readme file. Notably, all captured data is benign, devoid of any indications of malware. This dataset serves as a valuable resource for understanding IoT device behavior and network traffic patterns in real-world contexts.

Instructions: 

Further details about the method we used to generate, capture and label the IoT device network traffic can be found in our paper below:

[1] Jeffrey Adjei, Nur Zincir-Heywood, Biswajit Nandy, Nabil Seddigh; Identifying IoT Devices: A Machine Learning Analysis Using Traffic Flow Metadata; in the IEEE/IFIP Network Operations and Management Symposium (NOMS), AnNet at NOMS 2024.

The .pcap files, for each device, can be extracted from the .zip and ready to use.

Please contact one of these authors to get access to the source code: jeffrey.adjei@dal.ca or zincir@cs.dal.ca.

Please refer to the README file for further information.