  This study investigates the efficacy of machine learning models, specifically
Random Forest, in anomaly detection systems when trained on complete flow
records and tested on partial flow data. We explore the performance disparity
that arises when models are applied to incomplete data typical in real-world,
real-time network environments. Our findings demonstrate a significant decline
in model performance, with precision and recall dropping by up to 30\% under
certain conditions when models trained on complete flows are tested against
partial flows. Conversely, models trained and tested on consistently complete
or partial datasets maintain robustness, highlighting the importance of dataset
consistency in training. The study reveals that a minimum of 7 packets in the
test set is required for maintaining reliable detection rates. These results
underscore the need for tailored training strategies that can effectively adapt
to the dynamics of partial data, enhancing the practical applicability of
anomaly detection systems in operational settings.
