A feature-hybrid malware variants detection using CNN based opcode embedding and BPNN based API embedding

Being able to detect malware variants is a critical problem due to the potential damages and the fast paces of new malware variations. According to surveys from McAfee and Symantec, there is about 69 new instances of malware detected in every minutes, and more than 50% of them are variants of existing ones. Such a large volume of diversified malware variants has forced researches to investigate new methods based on common behavior patterns using machine learning.