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

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