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基于图结构密点抽取的辅助定密模型研究

于淼 郭松辉 宋帅超 杨烨铭

信息安全研究2025,Vol.11Issue(5):473-480,8.
信息安全研究2025,Vol.11Issue(5):473-480,8.DOI:10.12379/j.issn.2096-1057.2025.05.10

基于图结构密点抽取的辅助定密模型研究

Research on Auxiliary Classification Model Based on Extracting Key-points of Graph Structure

于淼 1郭松辉 2宋帅超 2杨烨铭2

作者信息

  • 1. 中国人民解放军网络空间部队信息工程大学密码工程学院 郑州 450001||中国人民解放军95861部队 甘肃酒泉 735018
  • 2. 中国人民解放军网络空间部队信息工程大学密码工程学院 郑州 450001
  • 折叠

摘要

Abstract

Auxiliary secret classification is a special text classification task that divides undecided encrypted text into different levels of confidentiality.,In order to solve the problems of the traditional method,such as weak feature representation and extraction ability and low interpretability of the classification process,key-points feature representation method based on graph structure was proposed.On that basis,an auxiliary secret classification model based on key-points extraction was further proposed,so as to enhance the ability of secret point features in describing the confidential matters,thus the performance of the auxiliary classification model is enhanced.Specifically,this paper deeply analyze the characteristics of classification rules,constructs the key-points template with reference to text representation method of the graphic structure,extracts the key-points and calculates the confidence level of the key-points of the text to be classified,and obtains the secret level prediction results and the classification basis items through the filtered effective key-points.The experimental result on the ACD indicates that the accuracy and recall rate of this model are 10%and 7%higher than those of BERT and TextCNN,which verifies the effectiveness of key-points feature representation method based on the graph structure.

关键词

图结构密点/密点置信度/辅助定密/定密规则/密点抽取

Key words

graph structure key-points/confidence of the key-points/auxiliary classification/classification rules/extracting key-points

分类

信息技术与安全科学

引用本文复制引用

于淼,郭松辉,宋帅超,杨烨铭..基于图结构密点抽取的辅助定密模型研究[J].信息安全研究,2025,11(5):473-480,8.

信息安全研究

OA北大核心

2096-1057

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