## 4 Results and Discussion
### Android Malware Detection Performance Comparison of FPA, Cas_FPA and RoF_FPA
As shown in Table 13, Cas_FPA and RoF_FPA recorded +0.58% and +0.12% increments in detection accuracy values and 31.25% and 6.25% reductions, respectively, in FPR values over FPA. Based on the analyses presented here, it can be deduced that the enhanced variants (Cas_FPA and RoF_FPA), especially Cas_FPA, are more effective in detecting Android malware than FPA. In other words, the meta-learners (cascading generalization and rotation forest) that were deployed amplified the detection performance of FPA. For generalizability, the detection performance of FPA, Cas_FPA, RoF_FPA, Cas_FPA+SMOTE, and RoF_FPA+SMOTE were compared with that of existing state-of-the-art Android detection models. Tables 14 and 15 display the detection performance of the proposed methods and existing models on the Malgenome and Drebin datasets, respectively. In Table 14, the results of the proposed methods are compared with findings from [PERSON] and [PERSON] (2017); [PERSON], [PERSON], [PERSON], and [PERSON] (2018); [PERSON], [PERSON], and [PERSON] (2019) (DT); and [PERSON], [PERSON] and [PERSON] (2018). Specifically, [PERSON] and [PERSON] (2017) developed a kNN-based Android malware model with a detection accuracy of 94% and an F-measure value of 0.940. SAFEDroid, developed by [PERSON], [PERSON], and [PERSON] (2018), achieved a detection accuracy of 98.30% and an FPR value of 0.02. Additionally, the Bayesian-based Android malware model by [PERSON], [PERSON], [PERSON], and [PERSON] (2018) had a detection accuracy of 92.10% and AUC and FPR values of 0.972 and 0.061, respectively. All these methods were trained and tested using the Malgenome dataset. Clearly, the proposed models (FPA, Cas_FPA, and RoF_FPA) showed significant improvement over the existing Android malware detection solutions. Furthermore, as presented in Table 15, the proposed methods were compared with findings from [PERSON], [PERSON], [PERSON], and [PERSON] (2018); [PERSON], [PERSON], and [PERSON] (2019); [PERSON], [PERSON], and [PERSON] (2019); [PERSON], [PERSON], and [PERSON] (2019); [PERSON] and [PERSON] (2020); and [PERSON], [PERSON], [PERSON], and [PERSON] (2020). These existing Android malware detection models were trained and tested using the Drebin dataset used in the \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\_FPA & 99.00 & 0.999 & 0.990 & 0.990 & 0.990 & 0.990 & 0.018 \\ * 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 \\ [PERSON] and [PERSON] (2017) & 94.00 & - & 0.940 & 0.950 & 0.950 & - & - \\ [PERSON] et al. (2018) & 92.10 & 0.972 & - & 0.937 & - & 0.904 & 0.061 \\ [PERSON] et al. (2018) (DT) & - & - & - & - & - & 0.916 & - \\ [PERSON], [PERSON] and [PERSON] (2019) (RF) & - & - & - & - & - & 0.967 & - \\ [PERSON], [PERSON] and [PERSON] (2018) & 98.30 & - & - & - & - & - & 0.020 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Detection performances of proposed methods and existing models on the Malgenome dataset. present research. An Android malware detection model-based similarity graph proposed by [PERSON], [PERSON], [PERSON], and [PERSON] [73] had an F-measure value of 0.869, which is lower than the F-measure values of the proposed models. [PERSON], [PERSON], and [PERSON] [64] developed a tree-based model with a detection accuracy of 97.92%. Additionally, [PERSON], [PERSON], and [PERSON] [74] suggested an ensemble of classification and clustering (EC2) method with an F-measure value of 0.970. [PERSON], [PERSON], and [PERSON] [75] utilized a feature-selection-based framework for Android malware detection with a detection accuracy value of 94%.