# A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System
###### Abstract
Their approach targeted malware families obtained from images of malware codes with high accuracy. Another classification mechanism was developed by [PERSON] et al. [8], which combines malware records with byte information. Further, a design was created in [11]. The design used deep convolutional neural networks and achieved 98.47% precision. A system was proposed in [12] that combines a recurrent neural network (RNN) and a CNN. It achieved good results on a single dataset. Analysis tools have also gained popularity over the past years. A malware analysis tool was introduced by [13]. The tool utilizes an environment to check malware samples and their corresponding system calls. A strategy investigated by [14] targeted malware behavior by focusing on system information exchange. Additionally, architecture proposed in [15] emphasized categorization and utilized normal and harmful software analysis. Additional malware recognition approaches such as [16] were investigated, which concentrated on the behavioral traits of malware. Similarly, a technique proposed by [17] utilized invariant modeling to obtain a representation of the graph. In recent years, the incorporation of artificial intelligence and machine learning algorithms has gained popularity for malware detection applications. [PERSON] et al. have explored the efficacy of a hybrid approach utilizing convolutional neural networks (CNN) and long short-term memory (LSTM) for malware classification, demonstrating promising results [18]. Concurrently, neural network-based methodologies have gained prominence in the context of Android malware detection, as exemplified in the studies by [PERSON] et al. [19] and [PERSON] and [PERSON] [20]. Given the aforementioned literature analysis, it is clear that using artificial intelligence and machine learning to detect malware has grown in popularity and demonstrated benefits. Positive effects have also been seen through visual depiction. Nonetheless, it is crucial to remember that various file formats still call for various properties in order to identify malware. Given this, the following is a summary of the work's goals and objectives:  
* To identify a universal image transformation method that works across multiple file formats, facilitating the development of a unified model with simplified resource requirements for detecting malware across multiple formats. * To develop a feature extraction approach that captures vital underlying data information from files. * To conduct a comprehensive assessment of various neural network models, including conventional and compact networks, for effective transfer learning in the specified application. * To establish a foundation for future research expansion to encompass additional file formats, such as audio (MP3) and video, broadening the scope of the study. Hence, it can be inferred that the proposed approach differs from existing methods by adopting a unified model update and retraining process, avoiding the need to retrain each file type separately. This approach streamlines the workflow, reduces redundancy, and conserves resources, resulting in more convincing results. Its suitability for malicious file identification stems from its ability to efficiently address diverse requirements, offer scalability, and provide cost-effective solutions. Our study examines different pre-trained compact and regular networks, in addition to series and directed acyclic graph architectures, in malware classification. Our approach utilizes grayscale transform-based features as a standardized feature set to classify malware across PDFs, Microsoft Office Documents, and Windows Portable Executable files. We incorporate multiple datasets in our study, such as Zenodo, Jonaslejon, Clean DOC files, Clean XLS files, Clean PDF files, Dike, and Malimg, in order to evaluate their accuracy.