基于卷积神经网络的大数据敏感信息监控系统的设计

Design of a Big Data Sensitive Information Monitoring System Based on Convolutional Neural Networks

大数据背景下敏感信息管理面临着新的挑战,有效监控敏感信息难度越来越大。针对传统监测研判手段和方法存在分类精度低、耗时长、存在误判、难于溯源定位、取证难等特点,本文研究卷积神经网络模型及朴素贝叶斯算法在大数据背景下对敏感信息的分类、识别和监控上的应用,采用TensorFlow与Keras深度学习的框架,利用朴素贝叶斯算法进行敏感信息分类,进而构建一个快速准确的将敏感信息进行识别监控数字化显示系统,该系统具备分类精度高,耗时短,误判少,能溯源的优势。

Sensitive information management is facing new challenges in the context of big data,The difficulty of effectively monitoring sensitive information is increasing.In response to the characteristics of low classification accuracy,long time consumption,misjudgment,difficulty in tracing and locating sources,and difficulty in obtaining evidence in traditional monitoring and judgment methods,This article investigates the application of convolutional neural network models and naive Bayesian algorithms in the classification,recognition,and monitoring of sensitive information in the context of big data,Adopting the framework of TensorFlow and Keras,using naive Bayesian algorithm for sensitive information classification,and constructing a fast and accurate digital display system for identifying and monitoring sensitive information.The system has the advantages of high classification accuracy,short time consumption,less misjudgment,and traceability.