# An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection
## 6 Experimental Analysis and Results
### Experimental Results and Analysis
Similarly, each colored bar represents one algorithm's percentage of normal true positive rates. The bar diagram of Figure 9 represents the percentages of the attack true positive rate of six aforesaid algorithms ([PERSON] et al. 's algorithm [35], [PERSON] et al. 's algorithm [17], deep-RBF network [16], Bayes network [15], decision tree [14], and IFRSCAD) for different attribute sizes of the dataset Kinsune [53]. The bar diagram of Figure 10 represents the percentages of normal false positive rates of the six aforesaid algorithms ([PERSON] et al. 's algorithm [35], [PERSON] et al. 's algorithm [17], deep-RBF network [16], Bayes network [15], decision tree [14], and IFRSCAD) for different attribute sizes of the dataset Kitsune [53]. The bar diagram of Figure 11 represents the percentages of attack false positive rates of the six aforesaid algorithms ([PERSON] et al. 's algorithm [35], [PERSON] et al. 's algorithm [17], deep-RBF network [16], Bayes network [15], decision tree [14], and IFRSCAD) for different attribute sizes of the dataset Kitsune [53]. The bar diagram of Figure 12 represents the percentages of average true positive rates of the aforesaid six algorithms ([PERSON] et al. 's algorithm [35], [PERSON] et al. 's algorithm [17], deep-RBF network [16], Bayes network [15], decision tree [14], and IFRSCAD) for different attribute sizes of the dataset Kitsune [53]. Again, the bar diagram of Figure 13 represents the percentages of average true positive rates of the aforesaid six algorithms ([PERSON] et al. 's algorithm [35], [PERSON] et al. 's algorithm [17], deep-RBF network [16], Bayes network [15], decision tree [14], and IFRSCAD) for different attribute sizes of the dataset Kitsune [53]. The following observations can be drawn from the above tables and bar diagrams. * The decision-tree-based algorithm [14] has the poorest detection rate. It has 71.31-66.49% of normal TPR, 67.44-62.23% of attack TPR, 29.69-33.51% of normal FPR, and 32.56-37.71% of attack FPR for ascending order of attribute sizes (from 10-41) of the dataset KDDCUP'99 [52]. Similarly, it has 71.31-50.12% of normal TPR, 67.44-49.34% of attack TPR, 28.69-49.88% of normal FPR, and 32.56-50.56% of attack FPR for ascending order of attribute sizes (from 10-115) of the dataset Kitsune [53]. It shows that the algorithm has the poorest performances, which decreases with the increase in dimension size of the dataset. * The deep-RBF-network-based algorithm [16] is better than the decision-tree-based algorithm [14] and it has 94.25-90.25% of normal TPR, 90.23-85.25% of attack TPR, 5.75-9.75% of normal FPR, and 9.75-14.75% of attack FPR for ascending order of attribute sizes (from 10-41) of the dataset KDDCUP'99 [52]. Similarly, it has 94.25-81.21% of normal TPR, 93.11-80.56% of attack TPR, 5.75-18.79% of normal FPR, and 6.89-19.44% of attack FPR for ascending order of attribute sizes (from 10-115) of the dataset Kitsune [53]. * The Bayes-network-based algorithm [15] is better than the decision-tree-based algorithm [14] and the deep-RBF-network-based algorithm [16] in terms of detection rates. It has 95.87-93.13% of normal TPR, 90.87-83.49% of attack TPR, 4.13-6.87% of normal FPR, and 9.136-16.51% of attack FPR for ascending order of attribute sizes (from 10-41) of the dataset KDDCUP'99 [52]. Similarly, it has 95.87-80.55% of normal TPR, 94.8-79.53% of attack TPR, 4.13-19.45% of normal FPR, and 5.20-20.47% of attack FPR for ascending order of attribute sizes (from 10-115) of the dataset Kitsune [53]. Although the algorithm is quite efficient, its performance decreases with the increase in the dimension of the datasets. * [PERSON] et al. 's algorithm [17] is better than all the previous three algorithms as far as detection rate is concerned. It has 97.75-93.25% of normal TPR, 95.25-89.25% of attack TPR, 3.20-5.80% of normal FPR, and 4.25-10.75% of attack FPR for ascending order of attribute sizes (from 10-41) of the dataset KDDCUP'99 [52].