  Network Intrusion Detection Systems (NIDSs) detect intrusion attacks in
network traffic. In particular, machine-learning-based NIDSs have attracted
attention because of their high detection rates of unknown attacks. A
distributed processing framework for machine-learning-based NIDSs employing a
scalable distributed stream processing system has been proposed in the
literature. However, its performance, when machine-learning-based classifiers
are implemented has not been comprehensively evaluated. In this study, we
implement five representative classifiers (Decision Tree, Random Forest, Naive
Bayes, SVM, and kNN) based on this framework and evaluate their throughput and
latency. By conducting the experimental measurements, we investigate the
difference in the processing performance among these classifiers and the
bottlenecks in the processing performance of the framework.
