## 4 Results and Discussion
### Android Malware Detection Performance Comparision of FPA and Baseline Classifiers  
In this section, the Android malware detection performance of FPA is compared with that of the selected baseline classifiers on original and SMOTE-balanced Malgenome and Drebin datasets, as depicted in Section 3.3 (Scenario 1). Table 6 presents the detection performance of FPA and the selected baseline classifiers on the Malgenome dataset. FPA outperformed the baseline classifiers based on the evaluation metrics that were considered. Specifically, based on detection accuracy values, FPA had a 98.94% accuracy value, which is +3.15% higher than that of the best-performing base classifier, ADT, with a 95.89% detection accuracy. Just below ADT, the Bayesian models (NB and BN) also recorded good detection accuracy values of more than 92% detection accuracy. The high detection accuracy value achieved by FPA, even on the unbalanced Malgenome dataset, emphasizes its robustness and efficacy for Android malware detection. FPA achieved the highest AUC value, at 0.998, followed closely by ADT (0.991). In addition, FPA achieved a very good balance between precision and recall, recording an F-measure value of 0.989. This observation further affirms the detection performance consistency of FPA as compared to other experimented baseline classifiers. Although ADT had a good F-measure value, it is still inferior to FPA (+3.12%). Considering the misclassification rate, FPA obtained the lowest score in FPR, with only a 0.016 chance of misclassifying any instance. This very low FPR shows that FPA is not prone to the misclassification errors often observed in other base classifiers. It can also be observed that the Bayesian models (NB and BN) outperformed the rest of the base classifiers in terms of FPR. Figure 2 presents a graphical representation of the performance comparison. Similar performance patterns were observed on the Drebin dataset (Table 7), as FPA recorded superior detection performances based on accuracy (98.13%), AUC (0.997), F-measure (0.981), and FPR (0.025) values when compared with the baseline classifiers. It was also observed that the detection performances of the classifiers on the Malgenome dataset are somewhat better than those on the Drebin dataset. Among the base classifiers, ADT recorded the highest detection accuracy (93.73%), AUC (0.981), and F-measure (0.937) values and the lowest FPR (0.063). It can also be observed that \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Accuracy** & **AUC** & **F-Measure** & **Precision** & **Recall** & **TPR** & **FPR** \\ \hline \({}^{*}\) FPA & 98.94 & 0.998 & 0.989 & 0.989 & 0.989 & 0.989 & 0.016 \\ \({}^{*}\) Cas\_FPA & **99.45** & **1** & **0.994** & **0.994** & **0.994** & **0.994** & **0.008** \\ \({}^{*}\) RoF\_F\_FPA & 99.00 & 0.999 & 0.990 & 0.990 & 0.990 & 0.990 & 0.018 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Detection performances of FPA, Cas\_FPA, and RoF_FPA on the Malgenome dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Accuracy** & **AUC** & **F-Measure** & **Precision** & **Recall** & **TPR** & **FPR** \\ \hline NB & 85.61 & 0.945 & 0.945 & 0.945 & 0.945 & 0.856 & 0.144 \\ BN & 85.58 & 0.942 & 0.942 & 0.942 & 0.942 & 0.856 &  
the performance of ADT concerning accuracy (90.20%), AUC (0.922), F-measure (0.922), and FPR (0.077) values. That is, DTable showed a better performance than the Bayesian algorithms (BN and NB) on the Drebin dataset in contrast to the Malgenome dataset. Generally, all the algorithms achieved good performance on the original datasets, except CR and DS, the performances of which were relatively low. Figure 3 shows the Android malware detection performances of FPA and the baseline classifiers on the Drebin dataset. We then proceeded to investigate the performance of FPA and baseline classifiers on SMOTE-balanced Android malware datasets. The essence of the deployment of the SMOTE data sampling technique is to remove the inherent class imbalance problem in the Android malware datasets.