A Machine Learning Model for Detecting Malware Outbreaks Using Only a Single Malware Sample

Machine learning (ML) has become an important part of the modern cybersecurity landscape, where massive amounts of threat data need to be gathered and processed to provide security solutions the ability to swiftly and accurately detect and analyze new and unique malware variants without requiring extensive resources. Some machine learning algorithms are typically trained on a large dataset. Malware outbreaks pose a challenge for machine learning in security since samples are scarce during the critical first hours. In our research paper entitled "Generative Malware Outbreak Detection," we demonstrated how machine learning technology for security solutions can identify a malware variant not only from large quantities of malware samples but also from only a small handful of observable variants. But how effective is machine learning if the only information available is from a single sample?