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Measuring Throughput and Latency of Machine Learning Techniques for Intrusion Detection
Abstract
When evaluating the effectiveness of machine learning algorithms for intrusion detection, it is insufficient to only focus on their performance metrics. One must also focus on the overhead metrics of the models. In this study, the performance accuracy, latency, and throughput of seven supervised machine learning algorithms and a proposed ensemble model were measured. The study performs a series of experiments using two recent datasets, and two filter-based feature selection methods were employed. The results show that, on average, the naive bayes achieved the lowest latency, highest throughput, and lowest accuracy on both datasets. The logistics regression had the maximum throughput. The proposed ensemble method recorded the highest latency for both feature selection methods. Overall, the Spearman feature selection technique increased throughput for almost all the models, whereas the Pearson feature selection approach maximized performance accuracies for both datasets.
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