# A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System
###### Abstract  
This study introduces an innovative all-in-one malware identification model that significantly enhances convenience and resource efficiency in classifying malware across diverse file types. Traditional malware identification methods involve the extraction of static and dynamic features, followed by comparisons with signature-based databases or machine learning-based classifiers. However, many malware detection applications that rely on transfer learning and image transformation suffer from excessive resource consumption. In recent years, transfer learning has emerged as a powerful tool for developing effective classifiers, leveraging pre-trained neural network models. In this research, we comprehensively explore various pre-trained network architectures, including compact and conventional networks, as well as series and directed acyclic graph configurations for malware classification. Our approach utilizes grayscale transform-based features as a standardized set of characteristics, streamlining malware classification across various file types. To ensure the robustness and generalization of our classification models, we integrate multiple datasets into the training process. Remarkably, we achieve an optimal model with 96% accuracy, while maintaining a modest 5 MB size using the SqueezeNet classifier. Overall, our model efficiently classifies malware across file types, reducing the computational load, which can be useful for cybersecurity professionals and organizations. neural network; transfer learning; malware detection; grayscale; portable executable; PDF; MS Word; artificial intelligence; deep learning +
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Data mining for malware detection was initially introduced by [PERSON] et al. [3]. For malware identification, they made use of static properties like strings, byte sequences, and portable executable (PE) files. Following that, [PERSON] et al. introduced a unique fingerprinting method for identifying polymorphic worms based on structural data from a binary code's control flow graph (CFG) [4]. Malware detection methods were also examined and compared [5]. Finally, an improved version of CFG-based malware detection was proposed by [PERSON] et al. [6]. The authors attempted to bridge the gap between formal approaches that characterize malware activities and deep learning technology. Static analysis techniques have become increasingly popular in recent years for locating malware. In order to leverage machine learning techniques to identify malicious applications, a combination of permission requests with application programming interface (API) requests was recommended [7]. This approach can be used to assess and categorize unidentified application packages. The use of visual analysis approaches has just been put forth, which is a significant advancement in the detection of malware. It has greater detection accuracy and fewer characteristics than the conventional static analysis method [8]. The technique utilizing visualization technology can extract high-dimensional inherent information from data samples, in addition to inheriting the benefits of conventional malware detection technologies. The first to propose the idea of turning malware into grayscale graphics was [9]. A given malware file transforms into integers that can be looked at as an image. Conducting a more thorough exploration of the subject, we explore state-of-the-art malware classification techniques, such as that proposed by [PERSON] et al. [10].