A pixel-filled malware classification preprocessing method for Windows-based platforms

The proliferation of malware variants, fuelled by sophisticated packaging, polymorphism and emulation techniques, has escalated the threat to Internet security. These evolving malware variants are often able to evade traditional detection methods and render them ineffective. Visualisation techniques are able to present complex data in an intuitive manner, and thus have become a promising tool in the field of malware analysis. However, current deep learning-based visualisation techniques tend to suffer from texture feature variations during the pre-processing phase, thus limiting their effectiveness when dealing with complex malware samples. To address this challenge, our research proposes a novel visualisation-based approach for lightweight and fast malware classification for the Windows platform. This approach utilises pixel-filling techniques to mitigate the variation of texture features during preprocessing and incorporates modular design principles to improve the saliency of key features. Experimental results demonstrate the superiority of our approach, achieving 99.14% accuracy on the widely used Malimg dataset, outperforming existing methods.