DALHOUSIE NIMS LAB IoT ATTACK DATASET 2025-1

DALHOUSIE NIMS LAB ATTACK IOT DATASET 2025-1 dataset comprises of four prevalent types attacks, namely Portscan, Slowloris, Synflood, and Vulnerability Scan, on nine distinct Internet of Things (IoT) devices. These attacks are very common on the IoT eco-systems because they often serve as precursors to more sophisticated attack vectors. By analyzing attack vector traffic characteristics and IoT device responses, our dataset will aid to shed light on IoT eco-system vulnerabilities. A Raspberry Pi was utilized to launch the attacks, targetting the devices in a controlled environment and each attack lasted 50 minutes.

Each device's traffic of an attack is stored in individual .pcap files. For our research, we extract flows from these .pcap files using Tranalyzer2 flow analysis tool.. Within this folder, you will find folders named after each attack, each containing the nine device name along with attack type. These states are detailed through pcap files, labelled as attack_device.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. Notably, all captured data has attack signatures.