## 4 Experimental Results and Discussion
### Comparison and Discussion
Table 8 compares the proposed system's accuracy, precision, recall, and F1 score to that of the previous work. A malware classification technique based on opcode sequences was proposed by [PERSON] et al. [32]. The system converted malicious programs into grayscale images, used RNN for feature extraction, employed CNN for image classification, and achieved 99.5% accuracy on the BIG2015 dataset. Due to the high complexity of malware itself, we considered not only opcode sequences but also bytecodes and features of image texture. The suggested method constructed based on the XGBoost classification algorithm achieves 99.87% accuracy on the BIG2015 dataset. Therefore, it is more suitable for complex malware classification tasks. A deep learning-based malicious software classification system (MCFT-CNN) was proposed by [PERSON] et al. [42] that does not require manual feature analysis or the actual execution of malware. The system achieved 98.63% accuracy, 98.56% precision, 96% recall, and 97.22% F1-score on the BIG2015 dataset. We used a tree-boosting-based machine-learning classification system, improved the classification accuracy by 1.24%, and the model is interpretable, which is helpful for further research and analysis of malicious. A novel methodology was suggested by [PERSON] et al. [47] to efficiently identify and categorize malware by combining different types of features, including API sequences, bytecodes, and file structures. On the BIG2015 dataset, CNN is 99.75% accurate, and the F1-score is 99.51%. Our proposed fusion feature set achieves greater F1-score and accuracy on the lightweight classifier-XGBoost, separately increased by 0.12% and 0.19%. [PERSON] et al. [43] proposed a lightweight network model based on static analysis. It requires less feature analysis and lower system complexity, with an F1-score of 98.2% on the BIG2015 dataset and an accuracy of 99.56%. [PERSON] et al. [44] proposed a deep neural network-based visualization approach for classifying malware with 98.31% accuracy, 98.68% precision, 97.93% recall, and 98.3% F1-score on the BIG2015 dataset. A malware classification system based on data mining and visualization of PE files was proposed by [PERSON] et al. [45]. The system converts the malicious file binary into a gray image as the deep convolution neural network's input. We not only considered the image texture features of malware but also fused features from other categories, which is more beneficial for malicious family classification. We adopted the more interpretive XGBoost classification algorithm, and the classification accuracy reached 99.87%. A new cloud-based, three-component malware classification model with semi-supervised migratory learning was proposed by [PERSON] et al. [48]: training, prediction, and migration. The experimental outcomes on the BIG2015 dataset demonstrate that semi-supervised migration learning has a 96.9% accuracy rate. They used a hybrid classification model composed of XGBoost and RNN, while we only used a separate XGBoost classifier, which greatly reduces the model complexity and memory consumption while ensuring high classification accuracy. [PERSON] et al. [50] proposed a new data set (SOMLAP) based \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Approaches** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline RMVC [32] & 99.50 & - & - & - \\ MCFT and CNN [42] & 98.63 & 98.56 & 96.00 & 97.22 \\ RCNF [43] & 99.56 & - & - & 98.20 \\ SERLA [44] & 98.31 & 98.68 & 97.93 & 98.30 \\ MDMC [45] & 99.26 & - & - & - \\ ML and VT [46] & 97.73 & - & - & - \\ HYDRA [47] & 99.75 & - & - & 99.51 \\ XGBoost and RNN [48] & 96.90 & - & - & - \\ CNN and BILSTM [49] & 98.20 & - & - & 96.05 \\ ACO-DT [50] & 99.37 & & & \\
**CatBoost** & **99.76** & **99.66** & **98.93** & **99.28** \\
**LightGBM** & **99.84** & **99.77** & **98.55** & **99.66** \\
**XGBoost** & **99.87** & **99.87** & **99.58** & **99.70** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with other methods.