  Recent advances in deep learning renewed the research interests in machine
learning for Network Intrusion Detection Systems (NIDS). Specifically,
attention has been given to sequential learning models, due to their ability to
extract the temporal characteristics of Network traffic Flows (NetFlows), and
use them for NIDS tasks. However, the applications of these sequential models
often consist of transferring and adapting methodologies directly from other
fields, without an in-depth investigation on how to leverage the specific
circumstances of cybersecurity scenarios; moreover, there is a lack of
comprehensive studies on sequential models that rely on NetFlow data, which
presents significant advantages over traditional full packet captures. We
tackle this problem in this paper. We propose a detailed methodology to extract
temporal sequences of NetFlows that denote patterns of malicious activities.
Then, we apply this methodology to compare the efficacy of sequential learning
models against traditional static learning models. In particular, we perform a
fair comparison of a `sequential' Long Short-Term Memory (LSTM) against a
`static' Feedforward Neural Networks (FNN) in distinct environments represented
by two well-known datasets for NIDS: the CICIDS2017 and the CTU13. Our results
highlight that LSTM achieves comparable performance to FNN in the CICIDS2017
with over 99.5\% F1-score; while obtaining superior performance in the CTU13,
with 95.7\% F1-score against 91.5\%. This paper thus paves the way to future
applications of sequential learning models for NIDS.
