- Citation Author(s):
- Submitted by:
- Burak Kantarci
- Last updated:
- Mon, 09/26/2022 - 13:39
- Data Format:
- Link to Paper:
The dataset has been developed in Smart Connected Vehicles Innovation Centre (SCVIC) of the University of Ottawa in Kanata North Technology Park.
In order to define a benchmark for Machine Learning (ML)-based Advanced Persistent Threat (APT) detection in the network traffic, we create a dataset named SCVIC-APT-2021, that can realistically represent the contemporary network architecture and APT characteristics. Please cite the following original article where this work was initially presented:
Jinxin Liu, Yu Shen, Murat Simsek, Burak Kantarci, Hussein Mouftah, Mehran Bagheri, Petar Djukic, “A New Realistic Benchmark for Advanced Persistent Threats in Network Traffic”, IEEE Networking Letters, vol. 4, no. 3, pp. 162-166, Sept. 2022, doi: 10.1109/LNET.2022.3185553.
Please see the descriptions and instructions in the attached pdf file.
- Training Set of the SCVIC-APT-2021 Dataset SCVIC-APT-2021-Training.csv (122.63 MB)
- Test Set of the SCVIC-APT-2021 Dataset SCVIC-APT-2021-Testing.csv (22.06 MB)