Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case Study. (arXiv:2401.16843v1 [cs.LG])

Cybersecurity remains a critical challenge in the digital age, with network
traffic flow anomaly detection being a key pivotal instrument in the fight
against cyber threats. In this study, we address the prevalent issue of data
integrity in network traffic datasets, which are instrumental in developing
machine learning (ML) models for anomaly detection. We introduce two refined
versions of the CICIDS-2017 dataset, NFS-2023-nTE and NFS-2023-TE, processed
using NFStream to ensure methodologically sound flow expiration and labeling.
Our research contrasts the …

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