云计算环境中移动网络低匹配度异质信息入侵感知预测算法

针对传统灰色神经网络组合预测算法对网络中入侵信息预测时, 缺乏对低匹配度异质信息的预处理过程, 未对信息入侵攻击意图进行预测, 存在预测准确率低以及入侵防御性能差等问题, 提出一种新的云计算环境中移动网络低匹配度异质信息入侵感知预测算法, 通过灰色模型对初始网络低匹配度异质信息进行预处理. 先采用基于元路径的低匹配度异质信息入侵感知预测算法得到入侵攻击意图矩阵, 再根据该矩阵获取入侵攻击意图函数关系, 实现低匹配度异质信息入侵攻击意图预测. 仿真实验结果表明, 该算法可全面预测信息入侵的意图和过程, 对入侵信息节点防御成功率约为85%, 误警率和漏警率较低, 并具有较高的预测精度.

Intrusion Awareness Prediction Algorithm for Mobile Networkwith Low Matching Degree Heterogeneous Information in Cloud Computing Environment#br#

When the traditional grey neural network combination prediction algorithm predicted the intrusion information in the network, itlacked the preprocessing process of low matching degree heterogeneous information, did not predict the intention of information intrusion attack, and had someproblems, such as low prediction accuracy and poor intrusion prevention performance. We proposed a new intrusion awareness prediction algorithm for mobile network with low matching degree heterogeneous information in cloud computingenvironment, and preprocessed the low matching degree heterogeneous informationof initial network by grey model. Firstly, the intrusion attack intention matrixwas obtained by using the intrusion awareness prediction algorithm with low matching degree heterogeneous information based on meta path. Then, according to the matrix, the function relationship of intrusion attack intention was obtained,and intrusion attack intention prediction with the low matching degree heterogeneous information was realized. The experimental results show that the proposed algorithm can fully predict the intention and process of the information intrusion, the success rate of the defense for intrusion information node is about 85%,the false alarm rate and the missed alarm rate are low, and the prediction accuracy is high.