## 4 Results and Discussion
### Android Malware Detection Performance Comparision of FPA and Baseline Classifiers
Moreover, the preference for SMOTE is based on its usage and efficacy, as reported in existing studies. Tables 8 and 9 present the experimental results of FPA and the baseline classifiers on the SMOTE-balanced Malgenome and Drebin datasets, respectively. As presented in Table 8, FPA still outperformed the baseline classifiers on all performance metrics evaluated. Specifically, FPA had a detection accuracy value of 98.85% and an FPR as low as 0.011. However, based on evaluation metrics (AUC and F-measure), the detection performance of the baseline classifiers was comparable to FPA. This observation can be attributed to the deployment of data sampling (SMOTE) to address the class imbalance problem. That is, the detection performances of the classifiers evaluated in the experiment improved as compared to their respective detection performances on the original Malgenome dataset. In particular, CR (+7.9%) and DS (+5.7%) achieved the greatest improvement in accuracy values. Additionally, a notable improvement in accuracy was observed in NB (+2.9%), BN (+1.9%), and DTable (+2.0%). ADT (+0.2%) recorded a slight improvement in its detection accuracy. Regardless, the detection accuracy performance of FPA is still superior. Concerning AUC values, DTable (+8.2%) achieved the greatest improvement, closely followed by NB (+7.34%), CR (+7.29%) and BN (+7.22%). Slight improvements were noticed in the AUC values of ADT (+0.20%), DS (+0.12%), and FPA (+0.10). A similar occurrence was observed with F-measure, as CR (+7.29%), DS (+5.31%),  
Figure 2: Android malware detection performance of FPA and baseline classifiers on the Malgenome dataset. NB (2.92%), DTable (2.74%), and BN (1.94%) had improved F-measure values. However, concerning the FPR metric, FPA (\(-\)31.25%) achieved the greatest improvement, with a reduction of 31.25% in FPR value. It should be noted that the lower the FPR value, the better the detection performance. Similarly, ADT (28.57%), DTable (4.55%), and NB (2.08%) exhibited a relative reduction in their FPR values. In general, the detection performances of FPA and the baseline classifiers improved on the SMOTE-balanced Malgenome dataset, but FPA still achieved the best overall performance. Figure 4 illustrates the Android malware detection performances of FPA and the baseline classifiers on the SMOTE-balanced Malgenome dataset. Furthermore, on the SMOTE-balanced Drebin dataset (Table 9), FPA still achieved the highest detection performance results in all performance measures considered. A similar finding to that observed in the experimental results on the SMOTE-balanced Malgenome dataset was also observed in the SMOTE-balanced Drebin dataset. That is, all classifiers recorded an improvement in their respective detection performance. As shown in Table 5, FPA (+0.24%), CR (+6.02%), DS (+5.48%), NB (+3.87%), BN (+3.38%), and DTable (+2.45%) had improved accuracy values when compared to their respective detection performances on the original Drebin dataset. Additionally, concerning AUC values, the Bayesian models (NB (+14%), BN (+14%), CR (+11.11%), and DTable (+5.86%)) each recorded significant improvement. The analysis based on the F-measure metric showed similar findings. Both FPA and the base classifiers had improved F-measure values, except those of ADT, for which results remain unchanged. FPA and DTable had a 36.00% and 68.83% reduction in FPR values, respectively. Other classifiers (NB, BN, DS, ADT, and CR) also had an increased FPR value. Figure 5 graphically represents the detection performances of FPA and baseline classifiers on the SMOTE-balanced Drebin dataset. Figure 3: Android malware detection performance of FPA and baseline classifiers on the Drebin dataset. Based on the foregoing experimental results on the Malgenome and Drebin datasets, the following findings were observed:  
Figure 4: Android malware detection performance of FPA and baseline classifiers on the SMOTE-balanced Malgenome dataset.