The complexity of files and objects with their increased breadth of file formats and sizes has presented a significant challenge to modern-day organizations seeking to improve detection and response processes for advanced malware threats. What you might call a “malware blob”, these threats are packed deep within data, hidden layers down and sometimes even out of sight from typical detection engines. For human analysts responsible for tracking and responding to threats, current detection engines offer only a “black box” perspective. In other words, they provide alerts, but offer little to no context as to what’s happening within the “blob” and human analysts struggle to understand and act on the risk they present effectively.
During this presentation, ReversingLabs addresses how to “escape the blob” by deploying modern machine learning techniques that help security teams better understand and defend against malware’s growing complexity and volume.
- Understand the evolution of threat detection and prediction
- See a “malware blob” example firsthand
- Learn new explainable machine learning models that improve human understanding and speed response to threats
- Discover how to improve SOC productivity and analyst malware knowledge over time