# An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection
## 6 Experimental Analysis and Results
### Experimental Results and Analysis  
The experiments were carried out in Matlab with Intel Core i7-2600 machine with 3.4 GHz, 8 MB Cache, 8 GB RAM, and 500 GB hard disc, running Windows 10, and the outcomes were analyzed with five prominent classification-based methods, namely, [PERSON] et al. 's algorithm [17], [PERSON] et al. 's algorithm [35], deep-RBF network [16], Bayes network [15], and decision tree [14]. The classifiers were built using the aforesaid dataset. The value of \(\alpha\) was assumed to be 0.4. The classifiers were then used to categorize any new instance as either normal traffic or an attack. For a variety of attributes sizes, the outcomes of all the aforesaid six methods were recorded. Data instances from various attacks were significantly out of proportion to normal data. Parameters such as true positive rate (TPR) and false positive rate (FPR) were utilized to estimate the effectiveness of the approaches and comparative analysis. A partial view of the results of the six algorithms describing the comparative analysis of normal true positive rate, attack true positive rate, normal false positive rate, and attack false positive rate for different sizes of attribute sets of the KDDCUP'99 dataset [52] is presented in Table 2 and Figures 2-7, respectively. The bar diagram of Figure 2 represents the percentage of normal true positive rate of six different algorithms, namely, [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, say 10, 20, and 41 of the dataset KDDCUP'99 [52]. Here, each colored bar represents one algorithm's percentage of normal true positive rates. The bar diagram of Figure 3 represents the percentages of 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 KDDCUP'99 [52]. The bar diagram of Figure 4 represents the percentages of normal false positive rates of a aforesaid algorithms for different attribute sizes of the dataset KDDCUP'99 [52]. Figure 4: Comparative analysis of normal false positive rates of different algorithms with KDDCUP’99. Figure 5: Comparative analysis of attack false positive rates of different algorithms with KDDCUP’99. Figure 6: Comparative analysis of average true positive rates of different algorithms with KDDCUP’99. Again, the bar diagram of Figure 7 represents the percentage of average false positive rate of the aforesaid six algorithms for different attribute sizes of the dataset KDDCUP'99 [52]. Similarly, a partial view of the results of the six algorithms describing the comparative analysis of normal true positive rate, attack true positive rate, normal false positive rate, and attack false positive rate for different sizes of attribute set of the Kitsune dataset [53] is presented in Table 3 and Figures 8-13, respectively. Figure 8: Comparative analysis of normal true positive rates of different algorithms with Kitsune [53]. Figure 10: Comparing analysis of normal false positive rates of different algorithms with Kitsune. Figure 9: Comparative analysis of attack true positive rates of different algorithms using Kitsune. Figure 11: Comparing analysis of attack false positive rates of different algorithms with Kitsune. Figure 12: Comparing analysis of average true positive rates of different algorithms with Kitsune. Figure 13: Comparing analysis of average false positive rates of different algorithms with Kitsune. The bar diagram of Figure 8 represents the percentages of normal true positive rate 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, say 10, 25, 50, 100, and 41 of the dataset Kitsune [53].