  Network Intrusion Detection Systems (NIDSs) are important tools for the
protection of computer networks against increasingly frequent and sophisticated
cyber attacks. Recently, a lot of research effort has been dedicated to the
development of Machine Learning (ML) based NIDSs. As in any ML-based
application, the availability of high-quality datasets is critical for the
training and evaluation of ML-based NIDS. One of the key problems with the
currently available datasets is the lack of a standard feature set. The use of
a unique and proprietary set of features for each of the publicly available
datasets makes it virtually impossible to compare the performance of ML-based
traffic classifiers on different datasets, and hence to evaluate the ability of
these systems to generalise across different network scenarios. To address that
limitation, this paper proposes and evaluates standard NIDS feature sets based
on the NetFlow network meta-data collection protocol and system. We evaluate
and compare two NetFlow-based feature set variants, a version with 12 features,
and another one with 43 features.
