关键词: 特征解析, 内容分割, 数据监测, 文件解析, 异常行为预测

Abstract: Aiming at the problems of small coverage, low monitoring accuracy and low automation of traditional data monitoring methods, a data lifecycle safety monitoring method driven by large data is put forward, which is based on feature analysis recognition model, content segmentation model, realtime data monitoring model, file analysis retrieval model and user abnormal behavior prediction model to monitor data security risk in realtime. It effectively guarantees the safe flow of data assets. After testing, the overall accuracy of sensitive data collection, sensitive page capture, sensitive flow monitoring and sensitive file parsing under this method is higher than 92%, and the accuracy of user’s sensitive behavior prediction is higher than 93%, which effectively improves the monitoring range and accuracy of sensitive data.

Key words: Feature parsing, content segmentation, data detection, document analysis, anomalous behavior prediction