Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems
Study examines how temporal features in NetFlow datasets influence ML-based intrusion detection accuracy, offering guidance for improving flow-based NIDS model design.
Summary written by editorial AI · Source link below
arXiv:2503.04404v3 Announce Type: replace-cross Abstract: This paper investigates the temporal analysis of NetFlow datasets for machine learning (ML)-based network intrusion detection systems (NIDS). Although many previous studies have highlighted the critical role of temporal features, such as inter-packet arrival time and flow length/duration, in NIDS, the currently available NetFlow datasets for NIDS lack these temporal features. This study addresses this gap by creating and making publicly
Editorial Analysis
SOC teams relying on ML-driven network detection could improve accuracy by re-evaluating the temporal features fed into their models.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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