###### Abstract
[PERSON] et al. [18] to extract static features, a convolutional neural network was utilized, deeply analyzed the development status and existing problems of information communication and network security, and adopted a process that uses reinforcement learning to ensure high network security. The experiment's findings demonstrate that this system performs superior to existing algorithms in this field. [PERSON] et al. [19] were the earliest to employ the BIG2015 dataset for malware classification, extracting malware binary and disassembly files' static features and combining multidimensional features to classify nine malware families with a 99.77% classification accuracy. [PERSON] et al. [20] proposed a supervised static detection model for Windows malware. On a sizable and labeled dataset (EMBER), the model is tested and trained based on the tree structured LightGBM classification algorithm. The experimental results show that using the initialized LightGBM classifier performs better than the unsupervised deep learning classification model on the same feature set. At the same time, the EMBER dataset, as the first standard dataset in the field of detection of malware, is beneficial to the research of malware detection in the field of machine learning. Although static analysis can achieve full path coverage without polluting the system, low detection rates and a high percentage of false positives are issues, and most malware classification systems use many different types of features, resulting in high system memory overhead and low detection efficiency. Given this, we proposed an efficient boosting-based Windows malware family classification system using multi-features fusion. This system performed feature fusion based on limited feature categories to reduce the time and memory overhead and improve the classification accuracy. Meanwhile, this system adopted an effective approach for classifying malware using tree boosting-based machine-learning algorithms, including XGBoost v1.5.1, LightGBM v3.3.5, and CatBoost v3.3.5. The classification accuracy of the XGBoost algorithm especially can be as high as 99.87%, and the logarithmic loss can be reduced to 0.007. The following constitutes the paper's original contribution:1. Combined plausible binary and assembly malware features using a forward feature stepwise selection method to produce the most efficient fused features. Experimental findings demonstrate that the XGBoost classifier's classification accuracy using the proposed fused features is 99.87%, which is higher than most existing malware classification models. 2. Using a limited number of 5530 features makes the system much faster with high accuracy and easier to use for large-scale malware classification tasks. 3. The LightGBM and CatBoost-based models were applied for the first time to process malware classification tasks. Both models obtained over 99.7% accuracy. Here is the remainder of the essay: An overview of the connected works is given in Section 2. The specifics of the suggested methodology are presented in Section 3. The experimental findings are covered in Section 4, and this paper's conclusions and recommendations are provided in Section 5.