## 4 Performance Validation  
The proposed model is simulated using the Python 3.6.5 tool on PC i5-8600k, GeForce 1050 Ti 4 GB, 16 GB RAM, 250 GB SSD, and 1 TB HDD. The Android malware detection results of the RHSODL-AMD approach are tested using the Andro-AutoPsy dataset [28], which comprises 9000 benign samples and 13,000 malware samples as represented in Table 1. The Andro-AutoPsy is an anti-malware system which depends upon the similarity matching of malware-centric and malware creator-centric information. It is used for classifying malware samples into similar subgroups by exploiting the profiles extracted from integrated footprints, which are implicitly comparable to different behavioral characteristics. It is useful for benign and malicious applications and for classifying malicious applications into similar behavior groups. For experimental validation, the dataset is split into 70:30 and 80:20 training sets (TRS) and testing sets (TSS). \begin{table}
\begin{tabular}{c c} \hline
**Class** & **No. of Samples** \\ \hline Benign & 9000 \\ Malware & 13,000 \\ Total Number of Samples & 22,000 \\ \hline \end{tabular}
\end{table} 
Table 1: Details of the dataset. The confusion matrices offered by the RHSODL-AMD technique is shown in Figure 4. The results show that the RHSODL-AMD technique accurately identified the benign and malignant samples under varying training set (TRS) and testing set (TSS). In Table 2, the overall Android malware detection results of the RHSODL-AMD technique are stated on 80:20 of TRS and TSS. On 80% of TRS, the results ensured that the RHSODL-AMD technique reaches the effectual classification of benign and malware samples. In addition, it is observed that the RHSODL-AMD technique offers an average \(accu_{bal}\) of 96.62%, \(prec_{n}\) of 96.70%, \(reca_{l}\) of 96.62%, \(F_{score}\) of 96.66%, and MCC of 93.32%. Followed by, on 20% of TRS, the RHSODL-AMD system offers an average \(accu_{bal}\) of 96.22%, \(prec_{n}\) of 96.26%, \(reca_{l}\) of 96.22%, \(F_{score}\) of 96.24%, and MCC of 92.48%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & \(Accuracy_{bal}\) & **Precision** & **Recall** & **F-Score** & **MCC** \\ \hline \multicolumn{6}{c}{Training Phase (80\%)} \\ \hline Benign & 95.79 & 96.30 & 95.79 & 96.04 & 93.32 \\ Malware & 97.45 & 97.10 & 97.45 & 97.27 & 93.32 \\ Average & 96.62 & 96.70 & 96.62 & 96.66 & 93.32 \\ \hline \multicolumn{6}{c}{Testing Phase (20\%)} \\ \hline Benign & 95.40 & 95.72 & 95.40 & 95.56 & 92.48 \\ Malware & 97.04 & 96.81 & 97.04 & 96.92 & 92.48 \\ Average & 96.22 & 96.26 & 96.22 & 96.24 & 92.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Android malware detection outcome of the RHSODL-AMD system on 80:20 of TRS/TSS. Figure 4: Confusion matrices of the RHSODL-AMD algorithm (**a,b**) TRS/TSS of 80:20 and (**c,d**) TRS/TSS of 70:30. The TACC and VACC of the RHSODL-AMD methodology on 80:20 of TRS/TSS are signified in Figure 5. The figure pointed out that the RHSODL-AMD system has revealed a better performance with higher values of TACC and VACC. It is noticeable that the RHSODL-AMD technique has reached superior TACC outcomes. The TLS and VLS of the RHSODL-AMD algorithm on 80:20 of TRS/TSS are given in Figure 6. The figure stated that the RHSODL-AMD system has exhibited a good performance with minimum values of TLS and VLS. It is observable that the RHSODL-AMD technique has resulted in decreased VLS outcomes. In Table 3, an overall Android malware detection outcome for the RHSODL-AMD system is stated at 70:30 of TRS and TSS. The outcome ensured that the RHSODL-AMD technique gains effective classification of benign and malware samples. On 70% of TRS, it is evident that the RHSODL-AMD system provides an average \(accu_{bal}\) of 99.05%, \(prec_{n}\) of 99.02%, \(rec_{q}\) of 99.05%, \(F_{score}\) of 99.03%, and MCC of 98.07%. Moreover, on 30% of TSS, the RHSODL-AMD algorithm offers an average \(accu_{bal}\) of 98.57%, \(prec_{n}\) of 98.69%, \(rec_{q}\) of 98.57%, \(F_{score}\) of 98.63%, and MCC of 97.27%.