## 4 Results and Discussion
### Android Malware Detection Performance Comparison of FPA, Cas_FPA and RoF_FPA  
In this section, the Android malware detection performance of FPA is compared with that of its enhanced variants (Cas_FPA and RoF_FPA) on original and SMOTE-balanced Malgenome and Drebin datasets, as shown in Tables 10-13, respectively. Thereafter, the detection performance of FPA, Cas_FPA, RoF_FPA, Cas_FPA+SMOTE, and RoF_FPA+SMOTE was compared with that of existing state-of-the-art Android malware detection models (Tables 14 and 15), as described in Section 3.3 (Scenario 2). Tables 10 and 11 present the detection performances of FPA, Cas_FPA, and RoF_FPA on the Malgenome and Drebin datasets. On the Malgenome dataset (Table 10), both Cas_FPA and RoF_FPA recorded superior detection performance over FPA. Specifically, Cas_FPA achieved a detection accuracy value of 99.45%, an AUC value of 1, an F-measure value of 0.994, and an FPR value of 0.008. RoF_FPA had a similar performance, with a detection accuracy value of 99%, an AUC value of 0.999, an F-measure value of 0.990, and an FPR value of 0.018. Notably, the AUC values of 1 and 0.999 recorded by Cas_FPA and RoF_FPA, respectively, indicate the effectiveness of the two models (Cas_FPA and RoF_FPA) in distinguishing Android malware applications from benign applications with approximately 100% certainty. Additionally, a very low FPR of almost zero recorded by Cas_FPA (0.008) showed its reliable counteraction towards false Android malware detection. Furthermore, on the Drebin dataset (Table 11), Cas_FPA and RoF_FPA outperformed FPA in terms of evaluation metrics. Cas_FPA (98.83%) and RoF_FPA (98.38%) showed a +0.71% and +0.25% increment, respectively, in detection accuracy value over FPA (98.13%). Concerning FPR value, significant improvements were observed; Cas_FPA (0.016) and RoF_FPA (0.023) had a 36% and 8% reduction in FPR, respectively, when compared with FPA (0.025). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Accuracy** & **AUC** & **F-Measure** & **Precision** & **Recall** & **TPR** & **FPR** \\ \hline \({}^{*}\) FPA+SMOTE & 98.37 & 0.997 & 0.984 & 0.984 & 0.984 & 0.984 & 0.016 \\ \({}^{*}\) Cas\_FPA+SMOTE & **98.94** & **0.999** & **0.989** & **0.990** & **0.989** & **0.011** \\ \({}^{*}\) RoF\_FPA+SMOTE & 98.49 & 0.998 & 0.985 & 0.985 & 0.985 & 0.985 & 0.015 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Detection performance of FPA, Cas_FPA, and RoF_FPA on the SMOTE-balanced Drebin dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Accuracy** & **AUC** & **F-Measure** & **Precision** & **Recall** & **TPR** & **FPR** \\ \hline \({}^{*}\) FPA+SMOTE & 98.85 & 0.999 & 0.989 & 0.989 & 0.989 & 0.989 & 0.011 \\ \({}^{*}\) Cas\_FPA+SMOTE & **99.42** & **1** & **0.994** & **0.994** & **0.994** & **0.994** & **0.006** \\ \({}^{*}\) RoF\_FPA+SMOTE & 99.07 & **1** & 0.991 & 0.991 & 0.991 & 0.991 & 0.009 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Detection performance of FPA, Cas_FPA, and RoF_FPA on the SMOTE-balanced Malgenome dataset. Furthermore, the detection performance of FPA, Cas_FPA, and RoF_FPA on the SMOTE-balanced Malgenome and Drebin datasets was compared. With this comparison, we aimed to ascertain whether the Cas_FPA and RoF_FPA algorithms can be further enhanced by deploying the SMOTE data sampling method. Tables 12 and 13 present the detection performances of FPA, Cas_FPA, and RoF_FPA on the SMOTE-balanced Malgenome and Drebin datasets, respectively. As presented in Table 12, it can be observed that Cas_FPA and RoF_FPA performed better than FPA on the SMOTE-balanced Malgenome dataset. In particular, Cas_FPA and RoF_FPA recorded +0.57% and +0.22% increments in detection accuracy values over FPA. Additionally, Cas_FPA and RoF_FPA had 45% and 18% reductions, respectively, in FPR values over FPA. In addition, a similar occurrence was observed on the SMOTE-balanced Drebin dataset.