## 4 Experimental Results and Discussion
### Comparison and Discussion  
In this section, to evaluate the efficacy of the combined feature set, we applied five common machine-learning techniques. The accuracy, precision, recall, and F1-score of the system suggested in this research are compared to those of the prior work. Figure 6 shows that compared with the feature set used in the existing work, the fusion feature set proposed in this paper performs better in classification methods for machine learning, such as KNN, SVM, random forest, AdaBoost, and XGBoost. The following is a detailed analysis and comparison. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category of Feature** & **Numbers** & **5-CV** \\ \hline  & **Accuracy** & **Logloss** \\ \hline
**C5 (Parameter optimization)** & **5530** & **0.9987** & **0.007** \\ C5 (No parameter optimization) & 5530 & 0.9979 & 0.009 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: XGBoost parameter optimization is used. Figure 5: Confusion matrix of fused features based on XGBoost classifier. [PERSON] et al. [37] utilized PCA to reduce the dimension of gray image representation converted from malware and used KNN to classify malicious software. For the BIG2015 dataset, the experimental findings demonstrate that the system has a classification accuracy of 96.6%. In contrast, our fusion feature set not only includes image information (Asm_Pixel), but also inconsistent opcodes, assembly codes, and other information that are more beneficial to classify malware. It proves that the fusion feature set containing multiple category features is more conducive to classifying malware families. Therefore, under the same KNN classification algorithm, this paper has achieved a classification accuracy of 99.23%. [PERSON] et al. [37] also used an SVM classifier to classify the BIG2015 dataset with an accuracy of 94.6%. [PERSON] et al. [38] proposed a new method to detect malware using digital signal processing strategies. To create a machine-learning music classification model from audio signals, [PERSON] et al. [38] converted program bytes into meaningful audio signals and used music information retrieval (MIR) methods. The model's classification accuracy on the BIG2015 dataset using the SVM classifier was 94.21%. However, the fused feature set proposed in this paper achieves 99.3% classification accuracy on the SVM classifier, which is higher than the above two methods. [PERSON] et al. [39] extracted a new control statement overlap-based feature to classify the BIG2015 dataset using a random forest classifier with an accuracy of 99.11%. [PERSON] et al. [40] employed malware parameters, such as resource size, virtual size, number of sections, and image version, to classify the BIG2015 dataset using the random forest classifier with an accuracy of 99.8%. [PERSON] et al. [38] achieved 95.87% and 95.65% classification accuracy, respectively, on the BIG2015 dataset using random forest and AdaBoost. By contrast, the random forest and AdaBoost classifier separately achieved 99.85% and 99.6% accuracy on the proposed fusion feature set. [PERSON] et al. [41] and [PERSON] et al. [19] applied the XGBoost classifier, which utilized features extracted from malware binaries and assembly files, to classify malware into their families with 99.83% and 99.77% accuracy. The proposed system used the XGBoost classification algorithm to achieve a higher accuracy of 99.87% on the fusion feature set. In summary, using our fusion feature set can vastly improve the classification accuracy of the classifiers. Compared to the current malware classification models, the amount, kind, and complexity of features and classification approaches are used. It is less complex in terms of the feature number, category, and classification algorithm. Therefore, our fusion feature set can be used to design a high-speed, high-accuracy, and efficient malware classification system. Figure 6: Our model is compared to recent research on the BIG2015 dataset [19; 37; 38; 39; 40; 41].