用AI动态调节网页安全数据渲染，提升高频率日志处理效率
AI-Assisted Adaptive Rendering for High-Frequency Security Telemetry in Web Interfaces
- 根据事件语义重要性动态调整刷新频率
- 减少45%-60%渲染开销，仍保持实时感知
- 适合需要处理海量安全日志的平台开发者
现代网络安全平台需实时处理并展示高频遥测数据，如网络日志、终端事件、告警和策略变更。基于静态分页或固定轮询的传统渲染技术在每秒超十万条事件的负载下会引发界面卡顿、丢帧或数据过时。本文提出一种AI辅助的自适应渲染框架，通过行为驱动的启发式规则与轻量级本地机器学习模型，动态调控视觉更新频率，优先展示语义相关事件，并对低优先级数据进行选择性聚合。实验验证表明，该方法可降低45%-60%的渲染开销，同时维持分析师对实时性的感知。
原文摘要 · Abstract (English)
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.
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