Alert Triage

Alert triage aims to efficiently prioritize and filter security alerts or warnings, reducing the overwhelming "alert fatigue" faced by analysts and improving response times. Current research focuses on developing machine learning models, including statistical learning and reinforcement learning, to automate this process, often leveraging techniques like large-scale clustering and transfer learning to identify patterns and prioritize truly critical events. These advancements are crucial for improving cybersecurity defenses and optimizing public health responses to events like extreme heat, enabling more effective resource allocation and potentially saving lives and reducing financial losses.