  Network Intrusion Detection Systems (NIDSs) datasets are essential tools used
by researchers for the training and evaluation of Machine Learning (ML)-based
NIDS models. There are currently five datasets, known as NF-UNSW-NB15,
NF-BoT-IoT, NF-ToN-IoT, NF-CSE-CIC-IDS2018 and NF-UQ-NIDS, which are made up of
a common feature set. However, their performances in classifying network
traffic, mainly using the multi-classification method, is often unreliable.
Therefore, this paper proposes a standard NetFlow feature set, to be used in
future NIDS datasets due to the tremendous benefits of having a common feature
set. NetFlow has been widely utilised in the networking industry for its
practical scaling properties. The evaluation is done by extracting and labeling
the proposed features from four well-known datasets. The newly generated
datasets are known as NF- UNSW-NB15-v2, NF-BoT-IoT-v2, NF-ToN-IoT-v2,
NF-CSE-CIC-IDS2018-v2 and NF-UQ-NIDS-v2. Their performances have been compared
to their respective original datasets using an Extra Trees classifier, showing
a great improvement in the attack detection accuracy. They have been made
publicly available to use for research purposes.
