开放式动态网络中可渗透路径预测方法仿真

针对机器学习、生物免疫以及条件概率算法下的三种可渗透路径预测方法存在的空间复杂度高、预测覆盖面小问题,提出基于贝叶斯算法的开放式动态网络可渗透路径预测方法。方法对贝叶斯算法进行描述,并基于贝叶斯算法设计可渗透路径预测方法,分析开放式动态网络可渗透过程,然后对可渗透数据进行采集并处理,提取可渗透特征,建立基于贝叶斯算法的预测模型,实现可渗透路径预测。结果表明,与机器学习、生物免疫以及条件概率算法下的三种可渗透路径预测方法相比,所提方法空间复杂度最低,预测覆盖面最大,最高可达98%。

Simulation of Permeable Path Prediction Method in Open Dynamic Networks

Aiming at the problems of high spatial complexity and small prediction coverage of three permeable path prediction methods based on machine learning,biological immunity and conditional probability,respectively,an open dynamic network permeable path prediction method based on Bayesian algorithm is proposed.The method is divided into two parts:the first part describes the Bayesian algorithm,and the second part designs the permeable path predic-tion method based on the algorithm.Firstly,the permeable process of open dynamic network is analyzed,then the permeable data are collected and processed to extract the permeable characteristics.Finally,the prediction model based on Bayesian algorithm is established to realize the permeable path prediction.The results show that,compared with machine learning,biological immunity and conditional probability algorithm,this method has the lowest spatial complexity and the largest prediction coverage,up to 98%.