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Most research using machine learning (ML) for network intrusion detectionsystems (NIDS) uses well-established datasets such as KDD-CUP99, NSL-KDD,UNSW-NB15, and CICIDS-2017. In this context, the possibilities of machinelearning techniques are explored, aiming for metrics improvements compared tothe published baselines (model-centric approach). However, those datasetspresent some limitations as aging that make it unfeasible to transpose thoseML-based solutions to real-world applications. This paper presents a systematicdata-centric approach to address the current limitations of NIDS research,specifically the datasets. This approach generates NIDS datasets composed ofthe most recent network traffic and attacks, with the labeling processintegrated by design.

Bridging the gap to real-world for network intrusion detection systems with data-centric approach