Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS

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 scal-able distributed stream processing system has been proposed in the literature. However, its performance, when machine-Iearning-based classifiers are implemented has not been comprehensively evaluated. In this study, we implement five representative clas-sifiers (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.