  The performance of machine learning based network intrusion detection systems
(NIDSs) severely degrades when deployed on a network with significantly
different feature distributions from the ones of the training dataset. In
various applications, such as computer vision, domain adaptation techniques
have been successful in mitigating the gap between the distributions of the
training and test data. In the case of network intrusion detection however, the
state-of-the-art domain adaptation approaches have had limited success.
According to recent studies, as well as our own results, the performance of an
NIDS considerably deteriorates when the `unseen' test dataset does not follow
the training dataset distribution. In some cases, swapping the train and test
datasets makes this even more severe. In order to enhance the generalisibility
of machine learning based network intrusion detection systems, we propose to
extract domain invariant features using adversarial domain adaptation from
multiple network domains, and then apply an unsupervised technique for
recognising abnormalities, i.e., intrusions. More specifically, we train a
domain adversarial neural network on labelled source domains, extract the
domain invariant features, and train a One-Class SVM (OSVM) model to detect
anomalies. At test time, we feedforward the unlabeled test data to the feature
extractor network to project it into a domain invariant space, and then apply
OSVM on the extracted features to achieve our final goal of detecting
intrusions. Our extensive experiments on the NIDS benchmark datasets of
NFv2-CIC-2018 and NFv2-UNSW-NB15 show that our proposed setup demonstrates
superior cross-domain performance in comparison to the previous approaches.
