DALHOUSIE NIMS LAB BENIGN DATASET 2024-2

DALHOUSIE NIMS LAB BENIGN DATASET 2024-2 dataset comprises data captured from Consumer IoT devices, depicting three primary real-life states (Power-up, Idle, and Active) experienced by everyday users. Our setup focuses on capturing realistic data through these states, providing a comprehensive understanding of Consumer IoT devices.

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, NFStream and Zeek). Within this folder, you will find folders named after each device, each containing the three states (Power-up, Idle and Active). These states are detailed through pcap files, labelled as state.iteration.pcap. All captures were conducted using IEEE 802.11 (Wi-Fi) in 2.4GHz channels.

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.