Datasets for the setting of Distributed, Anomaly-Based Intrusion Detection in Security-Oriented Edge Computing Environments
This repository contains several datasets for use with the DAISY framework, but also any other approaches that follow a distributed approach for intrusion detection, both anomaly- and misuse-based classification.
This is merely the overview of all the available datasets, for a more detailed description, see the respective directory and README.
This dataset includes the 5G communication monitoring indicators recorded throughout the project. The data was collected by our project partner HMF, and then processed and merged for further analysis.
This dataset is a transformation of the CIC-IDS2017 dataset (https://www.unb.ca/cic/datasets/ids-2017.html). It was split using the notebooks in the same directory into multiple CSV files.
This dataset was generated as a proof of concept for a master's thesis. It features two V2X communication devices and is a capture of real network traffic. The two hosts use the identifiers 2 and 5, as they are part of a bigger network with several additional machines.
This dataset is a live capture of eight extended road-side units (eRSU). The dataset and attacks were collected on real infrastructure. It features captures of eight multi-access edge computing (MEC) units in a distributed network. It was captured on the 22nd of July 2024, starting on the 21st at 20:00 CEST and ending on the 22nd at 20:00 CEST. The dataset is split into the eight hosts. Each host contains CSV files with 100.000 packets per file.
The datasets in this repository, except of the CIC IDS2017, are licensed under the Creative commons Attribution International Version 4.0 (CC BY 4.0)