Deep Multi-Task Learning for Malware Image Classification

arXiv:2405.05906v1 Announce Type: cross
Abstract: Malicious software is a pernicious global problem. A novel multi-task learning framework is proposed in this paper for malware image classification for accurate and fast malware detection. We generate bitmap (BMP) and (PNG) images from malware features, which we feed to a deep learning classifier. Our state-of-the-art multi-task learning approach has been tested on a new dataset, for which we have collected approximately 100,000 benign and malicious PE, APK, Mach-o, and ELF examples. Experiments with …

abstract art arxiv classification classifier cs.cr cs.lg deep learning detection features framework generate global image images malware malware detection multi-task learning novel paper png software state type