# Binary and Multi-Class Malware Threads Classification  
[PERSON]  
1 College of Computer Sciences and Information Technology, University of Anbar, Anbar 55431, Iraq 1  
[PERSON]  
2 Institute of Informatics and Computing in Energy, Universiti  
determining the specific malware family for a particular type of malware. There are two basic categories of malware analysis: static and dynamic. Figure 1 displays a regularly employed malware analysis taxonomy. Both manual and automated analysis is possible. Manual analysis necessitates subject expertise. On other hand, automatic analysis needs highly developed data science programming skills [6]. The primary mechanism of static analysis is to find binary files without running any software. It operates by taking malware binary's style signatures. Static analysis is one of the finest methods for identifying typical malware because it is quick and secure [7]. In contrast, during a dynamic analysis, a software's behavior is investigated, and from these findings, the software's intentions or purposes are inferred. Although it can detect sophisticated malware, it is time-consuming and prone to security threats [8]. In comparison to static analysis, dynamic analysis is a superior method, but it uses more time and memory and has scaling problems. The focus of our work is on static analysis. Recently, numerous studies have been conducted to identify malware utilizing image processing techniques including texture analysis, entropy, and image matrix. It has been noted that textural analysis continues to be used actively in malware detection via imaging techniques. An essential component of computer vision is texture analysis. Most surfaces have some roughness to them. Malware images from the same family tend to have fairly similar patterns and textures throughout most malware databases. It is clear that while the malware photos do not technically have repeating patterns, they do have a lot of \"texture,\" which can be used for automatic classification. There are striking visual similarities across malware images from the same family in terms of image texture [9]. [PERSON] et al. [10] was the first work to propose malware classification based on textural features. In order to compute texture features, they employ GIST [11; 12] which utilizes a wavelet decomposition of an image. Additionally, they demonstrated that texture analysis approaches using image processing may categorize malware faster than other malware classification approaches. However, the vast majority of MC approaches, which rely on texture analysis, have a number of fundamental flaws, including a low classification rate since they classify malware using inaccurate and onerous features. The huge feature vector dimension results in a significant computational burden [13] and consumption of resources. The requirement for discovering precise and practical features for increasing the MC performance following malware has been detected. Additionally, this is to identify the top malware classification techniques. Segmentation-based fractal texture analysis (SFTA) and Gabor filters are two widely used computational techniques for texture analysis that are effective for classifying and segmenting textures. Therefore, the proposed method would leverage relevant texture features, namely SFTA and Gabor as well as GDA and NB as Classifiers. Three steps make up the proposed malware analysis method: (a) malware conversion; (b) feature extraction; and (c) classification. We initially convert the RGB malware images into grayscale malware images for effective malware analysis. The SFTA and Gabor features are extracted from gray-scale images in the feature extraction step. Lastly, the classification is carried out by GDA and NB. Figure 1: Existing Taxonomy of Malware Analysis. The following are the contributions of the proposed method:  
1. To present an effective malware detection and classification method, SFTA and Gabor are extracted as distinctive feature vectors. 2.