Simulation of Potential Anomaly Recognition of Big Data Terminal Based on Spark Computing
Terminal information leakage is the main problem of big data security.The potential abnormal risk di-rectly affects the operation state of big data terminals.In this paper,a method of identifying potential anomaly of big data terminals was put forward based on spark computing.At first,the noise influence of the potential abnormal data was analyzed,and then the denoising algorithm was adopted to complete the preprocessing of the original terminal da-ta.After that,the data was input into the deep mining model of network big data for extracting the characteristics of potential abnormal data.Based on spark computing and adaptive fast decision tree,a parallel classification model was constructed.Finally,the extracted features were input into the model to realize the identification of potential anomalies.Simulation results show that the proposed method has higher recognition accuracy and efficiency,as well as bigger adaptability,indicating that the stability of the method is better.
Big dataFeature extractionPotential anomaly recognition
NETL
NSTL
万方数据
维普
