SCVIC-CIDS-2021 was created using the raw data in CIC-IDS-2018*, while this new dataset, SCVIC-CIDS-2022 is formed from NDSec-1** meta-data by following a similar procedure.
This dataset has been used in the following work:
J. Liu, M. Simsek, B. Kantarci, M. Bagheri, P. Djukic, "Bridging Networks and Hosts via Machine Learning-Based Intrusion Detection"; under review in IEEE Transactions on Dependable and Secure Computing.
SCVIC-CIDS-2021 is a novel dataset that combines network- and host-based data.
SCVIC-CIDS-2021 is derived from the meta-data (i.e., network packets, system logs and labeling information) from the well-known benchmark dataset, CIC-IDS-2018 (Iman Sharafaldin, Arash Habibi Lashkari, and Ali A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization”, 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018 ) .
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: