AI-Assisted Adaptive Rendering for High-Frequency Security Telemetry in Web Interfaces
This addresses performance issues for cybersecurity analysts using real-time monitoring platforms, but it is incremental as it builds on existing adaptive rendering techniques.
The paper tackled the problem of rendering high-frequency security telemetry in web interfaces, which causes UI freezes and stale data under heavy loads, and resulted in a 45-60% reduction in rendering overhead while preserving real-time responsiveness.
Modern cybersecurity platforms must process and display high-frequency telemetry such as network logs, endpoint events, alerts, and policy changes in real time. Traditional rendering techniques based on static pagination or fixed polling intervals fail under volume conditions exceeding hundreds of thousands of events per second, leading to UI freezes, dropped frames, or stale data. This paper presents an AI-assisted adaptive rendering framework that dynamically regulates visual update frequency, prioritizes semantically relevant events, and selectively aggregates lower-priority data using behavior-driven heuristics and lightweight on-device machine learning models. Experimental validation demonstrates a 45-60 percent reduction in rendering overhead while maintaining analyst perception of real-time responsiveness.