摘要
为了提高网络大数据的安全性能,进行Web入侵风险预测,提出基于非平稳性盲源分离的大数据的Web入侵检测模型进行风险预测估计。构建大数据的Web入侵信息测量模型,对Web大数据信息流进行二维信号拟合,采用非平稳性高斯独立平均统计量进行入侵信息判别,实现Web入侵风险预测模型改进设计。仿真结果表明,采用该方法进行大数据的Web入侵检测的准确检测概率较高,风险预测的精度高于传统模型。
In order to improve the security performance of network large data and predict the Web intrusion risk, the Web intrusion detection model based on the non-stationary blind source separation is proposed for risk prediction estimation. The big data Web intrusion information measurement model is constructed to perform two-dimensional signal fitting of Web data informa- tion flow. The non-stationary Gauss independent average statistical magnitude is used for intrusion infl^rmation discrimination to implement improvement and design of the Web intrusion risk prediction model. The simulation results show that the method has high detection probability for the big data Web intrusion detection, and higher risk prediction accuracy than that of the tradi- tional model.
出处
《现代电子技术》
北大核心
2017年第18期150-152,共3页
Modern Electronics Technique
基金
国家自然科学基金(60702075)
广东省高职高专云计算与大数据专业委员会教育科研课题(GDYJSKT14-04)
关键词
大数据
WEB入侵
风险预测
盲源分离
big data
Web intrusion
risk prediction
blind source separation
