大数据平台Web敏感信息保护监测方法研究  

Research on Monitoring Methods for Web Sensitive Information Protection on Big Data Platforms

作　　者：陈健[1] CHEN Jian(Shenyang Polytechnic College,Shenyang Liaoning 110045)

机构地区：[1]沈阳职业技术学院,辽宁沈阳110045

出　　处：《长江信息通信》2025年第2期143-145,共3页Changjiang Information & Communications

摘　　要：随着大数据平台的广泛应用,保护其中的敏感信息变得尤为重要。该研究将针对大数据平台Web敏感信息保护监测方法进行研究,以确保大数据平台能够实施有效的保护和监测措施。首先,采集大数据平台上的Web请求和数据流量,以获取实际的数据样本。这些数据样本将包含大量的用户行为和敏感信息传输情况。其次,使用特征提取技术来识别和提取大数据平台中的敏感信息。通过分析数据样本中的请求类型、参数、URL路径等特征,可以建立一个敏感信息特征库。最后,应用机器学习算法来实现对大数据平台Web敏感信息的保护监测。通过训练模型,能够从实时采集的数据中自动识别和标记敏感信息的出现,并做出相应的解决措施。分析实验结果可知:该文方法的敏感信息泄露率仅为2%左右,与传统方法相比本文方法具有较高的安全性。With the widespread application of big data platforms,protecting sensitive information has become particularly important.The study will focus on the research of web sensitive information protection monitoring methods for big data platforms,to ensure that the big data platform can implement effective protection and monitoring measures.Firstly,collect web requests and data traffic on the big data platform to obtain actual data samples.These data samples will contain a large amount of user behavior and sensitive information transmission situations.Secondly,use feature extraction techniques to identify and extract sensitive information from big data platforms.By analyzing the request types,parameters,URL paths,and other characteristics in the data sample,a sensitive information feature library can be established.Finally,machine learning algorithms are applied to protect and monitor sensitive information on the big data platform’s web.By training the model,being able to automatically identify and label the occurrence of sensitive information from real-time collected data,and make corresponding solutions.The analysis of the experimental results shows that:The sensitive information leakage rate of the method is only about 2%,compared with traditional methods,this method has higher security.

关 键 词：大数据平台 敏感信息 保护监测 数据流量 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]