网络威胁智能研判及应急调度分析数据集合

网络威胁智能研判算法：基于改进的随机森林算法，以威胁特征数据（如攻击频率、攻击源历史行为、目标系统脆弱性）为输入特征，构建威胁分类与概率预测模型。通过特征重要性评估（如攻击源 IP 的历史攻击次数权重最高），优化模型精度，预测准确率达 92% 以上；同时融入 LSTM 时序模型，分析威胁演化趋势（如某类攻击的周 / 月发生规律），实现未来 72 小时威胁发生概率预测

This intelligent cyberspace threat judgment and analysis algorithm is based on an improved Random Forest algorithm, which takes threat feature data including attack frequency, historical behavior of attack sources, and vulnerabilities of target systems as input features to construct a threat classification and probability prediction model. Through feature importance evaluation, for example, the historical attack count of the attack source IP carries the highest weight, the model accuracy is optimized, with the prediction accuracy exceeding 92%. Meanwhile, the LSTM time-series model is integrated to analyze threat evolution trends such as the weekly or monthly occurrence patterns of specific attack types, enabling the prediction of threat occurrence probability within the next 72 hours.

搜集汇总

背景与挑战

背景概述

该数据集规模为500MB，每周更新，专注于网络威胁智能研判和应急调度分析，采用改进的随机森林和LSTM算法实现高精度预测，准确率超过92%，并支持政务网络跨区域应急调度和事件等级预判，以提升网络安全响应效率。