大数据网络中虚假情报信息优化识别仿真研究
任敏
【期刊名称】《计算机仿真》 【年(卷),期】2017(034)003
【摘要】This paper proposes a method of optimization recognition for false intelligence information in large data network based on support data.Firstly,we defined intelligence data information as a group of feature space vector with multi-beam by means of cloud computing platform and extracted major feature.State space was also reconstructed.Then,we mapped state trajectory of major feature in feature space and classified each feature to build different information set of intelligence data.On that basis,we used average combination entropy to measure dispersion degree of dataset,while used average mutual information to measure dependency among different attribute of dataset.Moreover,we compared weight of information entropy of intelligence data with different types and selected information entropy with larger weight as intelligence under network.The rest was the false intelligence.Finally,we completed the optimization recognition of false intelligence information.Simulation results show that the method can improve recognition precision and efficiency.%在大数据网络中对虚假情报信息进行优化识别,可有效过滤虚假情报,净化网络空间.进行虚假情报信息识别时,应计算全部情报信息平均联合熵度量数据集的离散程度,利用不同类型情报数
大数据网络中虚假情报信息优化识别仿真研究
