A short review of open-source malware datasets and their implications for quantum machine learning

Abstract

The rapid development of quantum machine learning (QML) has created new opportunities for malware detection and classification. However, the effectiveness of QML-based malware research depends strongly on the availability and suitability of open-source malware datasets. Although many publicly accessible malware datasets have been designed for classical machine learning, their features and limitations have not been systematically scrutinised on quantum computing grounds. This short review provides a structured overview of publicly available open-source malware datasets: EMBER (Endgame Malware BEnchmark for Research), DREBIN Android malware dataset, CIC-MalMem-2022 (Canadian Institute for Cybersecurity Malware Memory Analysis Dataset 2022), and VirusShare, and evaluates their suitability for quantum machine learning applications. The datasets are analysed based on data modality, feature representation, mode of labelling, and accessibility. Key research issues are identified, including high dimensionality, class imbalance, uncertainty of labels, and dataset bias. Particular attention is given to limitations posed by near-term quantum devices, such as qubit availability, circuit depth restrictions, and data encoding overhead. Among the reviewed datasets, DREBIN offers the most favourable characteristics for near-term QML experimentation. Its sparse binary feature structure, after reduction to a 126-feature subset or via principal component analysis (PCA) to 15 - 50 components, yields compact vectors compatible with current qubit constraints. EMBER and VirusShare present the greatest encoding challenges owing to their very high dimensionality and lack of standardised feature representations. Angle encoding and hybrid quantum-classical approaches are identified as the most practical encoding strategies for current NISQ (Noisy Intermediate-Scale Quantum) devices. A five-criterion evaluation framework, covering feature dimensionality, encoding feasibility, dataset size, label quality, and preprocessing requirements, is proposed to guide dataset selection. The findings underscore the urgent need for purpose-built benchmark datasets designed with quantum hardware constraints in mind.