Leveraging deep learning and image conversion of executable files for effective malware detection: A static malware analysis approach

Abstract

The escalating sophistication of malware poses a formidable security challenge, as it evades traditional protective measures. Static analysis, an initial step in malware investigation, involves code scrutiny without actual execution. One static analysis approach employs the conversion of executable files into image representations, harnessing the potency of deep learning models. Convolutional neural networks (CNNs), particularly adept at image classification, have potential for malware detection. However, their inclination towards structured data requires a preprocessing phase to convert software into image-like formats. This paper outlines a methodology for malware detection that involves applying deep learning models to image-converted executable files. Experimental evaluations have been performed by using CNN models, autoencoder-based models, and pre-trained counterparts, all of which have exhibited commendable performance. Consequently, employing deep learning for image-converted executable analysis emerges as a fitting strategy for the static analysis of software. This research is significant because it utilized the largest dataset to date and encompassed a wide range of deep learning models, many of which have not previously been tested together.

Keywords

• artificial intelligence
• deep learning
• convolutional neural networks
• autoencoders
• transfer learning
• malware detection
• executable files