Scalable Malware Family Classification Using Quantum Kernel Based Machine Learning (opens in new tab)

The classification of malware families is a key challenge in cybersecurity, which enables threat attribution, analysis of attack operations, and the formulation of effective defense strategies. Emerging malware samples are becoming increasingly structurally similar and obfuscated, making accurate multiclass classification challenging for traditional machine learning models, especially when deployed at scale. In this research, we propose a scalab...