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Figure 5. Classification performance metrics (aggregated) vs. different classifiers. Figure 6. Classification performance metrics (One vs. Rest) vs. intrusion label. Table 1 presents the confusion matrix for the classification of five labels. The confusion matrix provides a sample count according to actual (ground-truth) and predicted labels. The table also provides totals and percentages along rows and columns. From Table 1, the number of correctly classified and misclassified samples can be easily obtained. In this table, we can see that the classification of the label R2L presents more difficulties due to the number of Doctoral Thesis: Novel applications of Machine Learning to NTAP - 138PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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