Early-Stage Anomaly Detection: A Study of Model Performance on Complete vs. Partial Flows

Pekár, Adrián and Jozsa, Richard (2025) Early-Stage Anomaly Detection: A Study of Model Performance on Complete vs. Partial Flows. In: IEEE European Symposium on Security and Privacy Workshops (EuroS&PW).

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Abstract

This study investigates the efficacy of machine learning models in network security threat detection through the critical lens of partial versus complete flow information, addressing a common gap between research settings and real-time operational needs. We systematically evaluate how a standard benchmark model, Random Forest, performs under varying training and testing conditions (complete/complete, partial/partial, complete/partial), quantifying the performance impact when dealing with the incomplete data typical in real-time environments. Our findings demonstrate a significant performance difference, with precision and recall dropping by up to 30 % under certain conditions when models trained on complete flows are tested against partial flows. The study also reveals that, for the evaluated dataset and model, a minimum threshold around 7 packets in the test set appears necessary for maintaining reliable detection rates, providing valuable, quantified insights for developing more realistic real-time detection strategies.

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