TITLE: Improving the Reliability of Network Intrusion Detection Systems through Dataset Integration 

ABSTRACT: This work presents Reliable-NIDS (R-NIDS), a novel methodology for Machine
Learning (ML) based Network Intrusion Detection Systems (NIDSs) that allows ML
models to work on integrated datasets, empowering the learning process with
diverse information from different datasets. Therefore, R-NIDS targets the
design of more robust models, that generalize better than traditional
approaches. We also propose a new dataset, called UNK21. It is built from three
of the most well-known network datasets (UGR'16, USNW-NB15 and NLS-KDD), each
one gathered from its own network environment, with different features and
classes, by using a data aggregation approach present in R-NIDS. Following
R-NIDS, in this work we propose to build two well-known ML models (a linear and
a non-linear one) based on the information of three of the most common datasets
in the literature for NIDS evaluation, those integrated in UNK21. The results
that the proposed methodology offers show how these two ML models trained as a
NIDS solution could benefit from this approach, being able to generalize better
when training on the newly proposed UNK21 dataset. Furthermore, these results
are carefully analyzed with statistical tools that provide high confidence on
our conclusions.
This work presents Reliable-NIDS (R-NIDS), a novel methodology for Machine
Learning (ML) based Network Intrusion Detection Systems (NIDSs) that allows ML
models to work on integrated datasets, empowering the learning process with
diverse information from different datasets. Therefore, R-NIDS targets the
design of more robust models, that generalize better than traditional
approaches. We also propose a new dataset, called UNK21. It is built from three
of the most well-known network datasets (UGR'16, USNW-NB15 and NLS-KDD), each
one gathered from its own network environment, with different features and
classes, by using a data aggregation approach present in R-NIDS. Following
R-NIDS, in this work we propose to build two well-known ML models (a linear and
a non-linear one) based on the information of three of the most common datasets
in the literature for NIDS evaluation, those integrated in UNK21. The results
that the proposed methodology offers show how these two ML models trained as a
NIDS solution could benefit from this approach, being able to generalize better
when training on the newly proposed UNK21 dataset. Furthermore, these results
are carefully analyzed with statistical tools that provide high confidence on
our conclusions.
Improving the Reliability of Network Intrusion Detection Systems through Dataset Integration
http://arxiv.org/abs/2112.02080v1