11th International Conference on ICTIS

Authors - Sanchit Prashant Joshi, Vedant Vipin Joshi, Aditya Arun Mangalekar, G.S.Mundada
Abstract - Malware classification is essential in cyber-security. It en ables prevention of threats by identifying and accurately classifying ma licious software. It also helps in understanding attacker behavior, enhanc ing threat intelligence, and improving the overall effectiveness of security systems. It is increasingly critical as adversaries now employ obfuscation techniques to avoid detection. Traditional models such as Convolutional Neural Networks (CNN) often struggle with such obfuscated malware samples. In this paper, we propose MalViT, a Vision Transformer (ViT) based framework for robust malware classification using grayscale image representations of malware binaries. The ViT is fine-tuned on a prepro cessed Malimg dataset. To evaluate the robustness of the model, real world obfuscation techniques such as Encryption, Dead code insertion, Random masking and Junk Padding are simulated. ViT model is initially f ine-tuned on the clean samples and later on a combination of the clean and obfuscated samples. Both models are evaluated on the clean and obfuscated test sets to highlight the robustness of the model. The final model achieved a combined accuracy of 94.52 % on both the clean and obfuscated samples. The results demonstrate that MalViT maintains a competitive performance under obfuscation. This project highlights the potential of ViTs in building resilient malware classification systems and provides a foundation for future work in transformer based architecture for malware analysis.