基于云的网络安全态势预测规则挖掘算法研究

Algorithm of Rule Mining for Network Security Situation Prediction Based on Cloud

现有网络安全态势预测算法对初始训练数据依赖性强,预测结果客观性差。提出了基于云的网络安全态势预测思想和基于云的网络安全态势预测规则挖掘算法。采用基于云模型的属性论域区间软划分方法解决了定性与定量转换中的区间硬性划分导致的边界元素内在联系丢失的问题。通过实验验证了算法的可行性和有效性。基于云的网络安全态势预测思想,不需要对预测算法进行数据训练,提高了网络安全态势预测的客观性。

The existing algorithms of network security situation prediction depended on the initial training data,and the objectivity of predicting results was insufficient.The thought of network security situation prediction based on cloud and an algorithm of network security situation prediction based on cloud were proposed.A soft partition method of attributes interval based on cloud model was proposed to solve the problem of that the internal relationship of boundary elements lost by hard partition method in quantitative changing to qualitative.Experimental results show that the algorithm is feasibility and effectiveness.The thought of network security situation prediction based on cloud doesn＇t need the initial data training,and the objectivity of predicting results is improved.