## 5 Results and Discussion
### Evaluation Results
#### 5.3.2 Performance Results of the Proposed Based on GoogLeNet  
In the second experiment, three classifiers: KNN, SVM, and ELM, are employed for classification after applying the deep GoogLeNet features approach to the Malimg datasets for feature extraction. Table 3 displays the collected findings for each malware family and the total average accuracy metric following the application of each classifier. In comparison to previous results, as shown in Table 2, the malware families with the highest number of variations, including Allaple.A and Allaple.L, have higher accuracy, especially using KNN and SVM Classifier, as shown in Table 3. This could be attributed to the dataset's separation into training data and testing data, excluding relative populations of each malware family. Most malware families have accuracy levels of 90 or above across all classifiers. It is important to note that two malware families (Allaple.A and Allaple.L) have accuracy levels below 80 as depicted in Figure 14, especially using the ELM Classifier. Consequently, the outcomes are based on unbalanced features and data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class ID**} & \multirow{2}{*}{**Family Name**} & \multicolumn{3}{c}{**Classification Accuracy (\%)**} \\ \cline{3-5}  & & **KNN** & **SVM** & **ELM** \\ \hline \#1 & Adialer.C & 98 & 99 & 98 \\ \hline \#2 & Agent.FYI & 100 & 99 & 98 \\ \hline \#3 & Allaple.A & 96 & 94 & 58 \\ \hline \#4 & Allaple.L & 99 & 84 & 78 \\ \hline \#5 & Alueron.gen!J & 100 & 97 & 96 \\ \hline \#6 & Autorun.K & 100 & 98 & 97 \\ \hline \#7 & C2 LOP.P & 100 & 97 & 97 \\ \hline \#8 & C2 LOP.gen!g & 100 & 98 & 98 \\ \hline \#9 & Dialplatform.B & 100 & 98 & 98 \\ \hline \#10 & Dontovo.A & 100 & 98 & 98 \\ \hline \#11 & Fakerean & 100 & 96 & 96 \\ \hline \#12 & Instantaccess & 100 & 95 & 94 \\ \hline \#13 & Lolyda.AA1 & 100 & 98 & 98 \\ \hline \#14 & Lolyda.AA2 & 100 & 98 & 98 \\ \hline \#15 & Lolyda.AA3 & 100 & 99 & 98 \\ \hline \#16 & Lolyda.AT & 100 & 99 & 99 \\ \hline \#17 & Malex.gen!J & 100 & 99 & 97 \\ \hline \#18 & Obfuscator.AD & 100 & 98 & 97 \\ \hline \#19 & Rbotfgen & 100 & 98 & 97 \\ \hline \#20 & Skintrim.N & 100 & 99 & 99 \\ \hline \#21 & Swizzor.gen!E & 100 & 98 & 98 \\ \hline \#22 & Swizzor.gen!I & 100 & 99 & 98 \\ \hline \#23 & VB.AT & 100 & 95 & 95 \\ \hline \#24 & Wintrim.BX & 100 & 98 & 99 \\ \hline \#25 & Yuner.A & 100 & 98 & 90 \\ \hline \multicolumn{3}{c}{Average} & 96.84 & 94.24 & 92.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of classification accuracy of three classifiers on deep feature across the Malimg dataset. According to the accuracy results, the KNN on an unbalanced datasets achieved a high classification accuracy of 96.84 percent using 10-fold cross-validation. KNN has the benefit of being very straightforward, not requiring a specific training phase, and being quite efficient for many applications, particularly when the training set is big. The outcomes showed that the SVM produces substantial values (94.24) close to the KNN optimal. ELM performed poorly when compared to other classifiers, including KNN and SVM. For this purpose, in the second findings section of this paper, we took the proposed model based on deep GoogLeNet features and the KNN classifier into consideration. This indicates that the performance results of the proposed methods are often consistent. The results indicated that our method can efficiently and precisely classify malware to their relevant families even when the training datasets are unbalanced. While removing the human feature engineering phase, the proposed classification method exhibits good precision and completion time that is equivalent to traditional solutions based on machine learning. The results show that the Google net CNN model's deep features have a significant impact, especially when it comes to certain classes, such Allaple.A and Allaple.L. These classes' overall accuracy in prior experiments were (86, 70, 85) & (88, 82, 87) and improved into (96, 94, 58) & (99, 84, 78).