MalScore: A Quality Assessment Framework for Visual Malware Datasets Using No-Reference Image Quality Metrics

The Internet has been progressively more integrated into daily life through its evolutionary stages, ranging from web1.0 to the current development of web3.0. These continued integrations broaden the attack surface that cybercriminals aim to exploit. The prevalence of cybercrimes, particularly malware attacks, has become increasingly sophisticated and made more accessible through dark web marketplaces. Including artificial intelligence (AI) within anti-virus solutions has challenged the traditional dichotomy of malware detection schemes, offering more accurate and holistic detection capabilities. Research has shown that transforming malware files into textured images offers resistance to obfuscation and the potential to detect zero days. This paper explores the application of image quality assessment (IQA) techniques in enhancing visual malware dataset curation. We propose a novel framework that applies a no-reference IQA algorithm to evaluate current datasets and offer guidance in future dataset curation. Using multiple popular datasets, our evaluation demonstrates that the proposed MalScore framework effectively differentiates dataset quality—for example, MalNet Tiny achieves the highest score of 95%, while the NARAD malicious-image subset scores 50%. Additionally, BRISQUE was the only IQA algorithm to exhibit a strong linear sensitivity to blur levels across datasets. These results highlight the practical utility of MalScore in assessing and ranking visual malware datasets and lay the groundwork for uniting IQA and visual malware detection in future research.