  Malware is one of the most dangerous and costly cyber threats to national
security and a crucial factor in modern cyber-space. However, the adoption of
machine learning (ML) based solutions against malware threats has been
relatively slow. Shortcomings in the existing ML approaches are likely
contributing to this problem. The majority of current ML approaches ignore
real-world challenges such as the detection of novel malware. In addition,
proposed ML approaches are often designed either for malware/benign-ware
classification or malware family classification. Here we introduce and showcase
preliminary capabilities of a new method that can perform precise
identification of novel malware families, while also unifying the capability
for malware/benign-ware classification and malware family classification into a
single framework.
