# Binary and Multi-Class Malware Threads Classification
## 2 Related Works  
This section provides an overview of earlier studies on malware detection (MD) and malware classification (MC) techniques. It can be seen that textural analysis is still used actively to find malware using image techniques.  
[PERSON] et al. [4] presented the MC method as reliant on Gabor Wavelet, GIST and DWT. Malware is categorized using a Support Vector Machine (SVM) classification technique. On the Malimg Dataset, the proposed algorithm underwent testing. [PERSON] et al. [5] presented malware classification as reliant on the first-order and GLCM-based second-order statistical texture features. The public Malimg malware Dataset was used to test the presented method. ELM is a classifier that has been used in the classification phase. [PERSON] et al. [14] presented the MC method as dependent on static and dynamic features. Multiple classification algorithms were used, including IBI, decision tree, and random forest. [PERSON] et al. [15] presented the MC method as reliant on visualized images and entropy graphs. Determining the similarities of entropy graphs has been used to find and classify malware. [PERSON] et al. [16] presented the MC method by using textural features which consisted of wavelet transform and Gabor transform. The KNN classifier was used in the classification stage. [PERSON] et al. [17] presented the MC method as reliant on dynamic, static, and content-oriented features. In the classification scenario, a fuzzy random forest and an SVM are classifiers that have been applied. [PERSON] et al. [18] presented the MC method by using structural information. The Assemble classifier was used in the classification stage. They employ the call graph method, which collects the features of each malware sample. [PERSON] and [PERSON] [19] presented malware detection (MD) based on GIST feature extraction technique. The model had a detection accuracy of 91.6%. The malware is categorized using a random forest classification technique. On the MaleVis Dataset, the proposed algorithm underwent testing. [PERSON] et al. [20] presented malware detection (MD) based on B2M (Binary mapping to image) algorithm, the SURF algorithm and the Local sensitive hashing (LSH) algorithm. The method had an 85% classification accuracy rate. [PERSON] et al. [21] presented the MC method as reliant on GIST and multi-layer LBP features. The proposed method experimented on the Malimg Database. The RF classifier was used in the classification stage. [PERSON] et al. [22] presented MC by using the global features and local features combined. Multiple classification algorithms were used, including support vector machine, random forest, and K-nearest neighbor. [PERSON] and [PERSON] [23] proposed the MC method based on local mean method. The ensemble learning classifier was used in the classification stage. [PERSON] et al. [24] presented the MC method as reliant on GIST, HOG (Histogram of Gradients) descriptors and their combination. Multiple classification algorithms were used, including j48, RBF kernel-based SMO, Random Forest, XGBoost and linear SVM.  
The majority of the MC techniques previously discussed are based on texture analysis techniques. In contrast to these MC techniques, the proposed method would leverage relevant texture features, namely SFTA and Gabor. These approaches' fundamental drawback is that they have a low classification rate since they classify malware using unreliable and cumbersome features. Another factor is a large feature vector dimension. As a result, the proposed technique lowers the risk of misclassification and increases classification accuracy. In addition, our method places more emphasis on machine learning classification to cut down on computing costs.