基于关联规则的态势预测方法
发布时间:2018-11-25 12:18
【摘要】:态势预测是网络态势感知的重要环节,可以为网络管理员提供必要的决策支撑。为了实现对网络的大数据管理模式,针对当前预测算法无法充分利用大数据优势的局限,提出了基于关联规则的态势预测方法。该方法综合考虑了大数据的特点和态势预测的需求,给出了方法的基本思想和实现流程。实验结果表明,提出的方法与传统预测方法相比,通过寻找数据间的关联物而不是非线性匹配来达到预测的目的,大大降低了计算的时间复杂度,提高了预测效率。
[Abstract]:Situation prediction is an important part of network situational awareness, which can provide necessary decision support for network administrators. In order to realize the big data management mode of the network, aiming at the limitation that the current prediction algorithm can not make full use of the advantage of big data, a situation prediction method based on association rules is proposed. This method considers the characteristics of big data and the demand of situation forecast, and gives the basic idea and realization flow of the method. The experimental results show that compared with the traditional prediction method, the proposed method can achieve the purpose of prediction by finding correlation objects between data rather than nonlinear matching, which greatly reduces the time complexity of calculation and improves the efficiency of prediction.
【作者单位】: 空军工程大学信息与导航学院;
【基金】:陕西省科技计划自然基金(2012JZ8005)
【分类号】:TP393.07;TP311.13
本文编号:2356077
[Abstract]:Situation prediction is an important part of network situational awareness, which can provide necessary decision support for network administrators. In order to realize the big data management mode of the network, aiming at the limitation that the current prediction algorithm can not make full use of the advantage of big data, a situation prediction method based on association rules is proposed. This method considers the characteristics of big data and the demand of situation forecast, and gives the basic idea and realization flow of the method. The experimental results show that compared with the traditional prediction method, the proposed method can achieve the purpose of prediction by finding correlation objects between data rather than nonlinear matching, which greatly reduces the time complexity of calculation and improves the efficiency of prediction.
【作者单位】: 空军工程大学信息与导航学院;
【基金】:陕西省科技计划自然基金(2012JZ8005)
【分类号】:TP393.07;TP311.13
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