IoT Emulated Dataset for ICMP/Ping Normal and Malicious Traffic
Authors/Creators
- 1. King Abdulaziz University
- 2. Mehran University of Engineering and Technology
Description
These datasets are related to Intrusion Detection System, Computer Network Traffic and IoT.
These datasets are generated for the purpose of differentiating ICMP/Ping normal and malicious traffic that are generated from an embedded device (IoT). The differentiation analysis is done using machine learning.
There are three types of files that depend on each module of our research framework. The data generation sequence is as follows:
The pcap files (network traffic) are generated first, the device used to generate the data is an ESP-01s. Afterwards, the pcap files are transformed into log files using the Zeek tool, the log files are then extracted and placed into CSV files.
The CSV files are labeled and ready for the Machine Learning process.
The publication reference for this work is here : https://doi.org/10.1109/ACCESS.2023.3327061
The code link: https://zenodo.org/badge/latestdoi/619245496
This version of the release (0.2.0) is for ping flood with spoofed IPs, however, the previous version (0.1.0) is for static IP
Notes
If you use this dataset, please cite it as below.
O. M. Almorabea, T. J. S. Khanzada, M. A. Aslam, F. A. Hendi and A. M. Almorabea, "IoT Network-Based Intrusion Detection Framework: A Solution to Process Ping Floods Originating From Embedded Devices," in IEEE Access, vol. 11, pp. 119118-119145, 2023, doi: 10.1109/ACCESS.2023.3327061.