The proliferation of ransomware has become a widespread problem culminating in numerous incidents that have affected users worldwide. Current ransomware detection approaches are limited in that they either take too long to determine if a process is truly malicious or tend to miss certain processes due to focusing solely on static analysis of executables. To address these shortcomings, we developed a machine learning model to classify forensic artifacts common to ransomware infections: ransom notes. Leveraging this model, we built a ransomware detection capability that is more efficient and effective than the status quo.
I will highlight the limitations to current ransomware detection technologies and how that instigated our new approach, including our research design, data collection, high value features, and how we performed testing to ensure acceptable detection rates while being resilient to false positives. I will also be conducting a live demonstration with ransomware samples to demonstrate our technology’s effectiveness. Additionally, we will be releasing all related source code and our model to the public, which will enable users to generate and test their own models, as we hope to further push innovative research on effective ransomware detection capabilities.