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Data Security Detection and Location Technology Based on DLP Network

The 2021 International Conference on Smart Technologies and Systems for Internet of Things(2022)

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摘要
Faced with a complex network environment, network security issues are getting more and more serious. Cyber attacks will not only leak user privacy, but also cause huge economic losses. In the face of massive network data, decision trees have become an effective method for detecting abnormal network data. The decision tree method trains a model on a large amount of data, classifies normal data and abnormal data, and detects network attacks more efficiently and accurately. This article aims to study DLP network data security detection and positioning technology. Based on the analysis of DLP trends, the development direction of intrusion detection, abnormal data classification algorithms and positioning technology, the KDD CUP1999 data set is selected as the experimental data set. These three methods, namely, decision tree, support vector machine, are used to detect the data set. The detection results show that the data detection rate and false alarm rate of the decision tree algorithm perform better among the three algorithms, and are suitable for network data security detection.
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关键词
DLP, Data security detection, Security positioning, Decision tree
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