异构复杂信息网络下的异常数据检测算法

Abnormal Data Detection Algorithm in Heterogeneous Complex Information Network

异构复杂信息网络承载着不同的协议和网络信道,并通过云储存实现资源调度,由此产生的异常数据会给网络信息空间带来安全威胁和存储开销,所以需要进行异常数据准确检测。传统的检测算法采用简化梯度算法进行异常数据检测,不能有效去除多个已知干扰频率成分的异常数据,检测性能不好。提出一种基于自适应陷波级联模型的异常数据检测算法。构建异构复杂信息网络系统模型,采用固有模态分解把异常数据信号解析模型分解为多个窄带信号,设计二阶格型陷波器结构,用多个固定陷波器级联抑制干扰成份,采用匹配投影法寻求优化特征解,找出所有匹配的特征点对,从而实现异常数据检测的改进。仿真实验表明,采用该算法进行异常数据检测时,信号幅值大于干扰噪声数据幅值;该算法提高了检测性能,具有较好的抗干扰性能。

Heterogeneous information network carries different protocols and network channels, and realizes the re- source scheduling through the cloud storage,resulting in abnormal data which can cause security threat and storage o- verhead to the network information space,so accurate detection of abnormal data is essential. The traditional detection algorithms use the simplified gradient algorithm for outlier detection, which cannot effectively remove multiple abnormal data of known interference frequency components, so the detection performance is not good. An abnormal data detection algorithm was proposed based on adaptive notch cascade model. The heterogeneous information network system model is constructed, and by using the intrinsic mode decomposition, the abnormal data signal analytical model is decomposed into multiple narrowband signals. Two order lattice notch filter structure is designed,and a plurality of fixing notch fil- ters cascade is used to suppress the interference components. Matching projection method is used to seek the optimized feature solution, and all matching feature point pairs are find out, realizing the improvement of outlier detection. Simula- tion results show that, when the algorithm is used in data detection, signal amplitude is larger than the amplitude of noise interference data. It can improve the detection performance,and anti-interference performance is better.