基于AI的自适应渲染用于Web界面中的高频安全遥测
AI-Assisted Adaptive Rendering for High-Frequency Security Telemetry in Web Interfaces
AI总结：
本文提出基于AI的自适应渲染框架，通过动态调节视觉更新频率、优先处理语义相关事件并选择性聚合低优先级数据，降低渲染开销并保持实时响应性。
摘要：
现代网络安全平台必须实时处理和显示高频遥测数据，如网络日志、终端事件、警报和策略变更。传统基于静态分页或固定轮询间隔的渲染技术在处理超过每秒数万条事件时失效，导致UI冻结、丢帧或数据过时。本文提出一个AI辅助的自适应渲染框架，通过行为驱动的启发式方法和轻量级设备端机器学习模型，动态调节视觉更新频率，优先处理语义相关事件，并选择性聚合低优先级数据。实验验证表明，该方法在保持分析师对实时响应感知的同时，将渲染开销减少了45-60个百分点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
