Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (5): 330-336.doi: 10.11896/jsjkx.240300162

• Information Security • Previous Articles     Next Articles

Study on Security Risk Relation Extraction Based on Multi-view IB

LI Xiwang1, CAO Peisong1, WU Yuying1, GUO Shuming2,3, SHE Wei1,2   

  1. 1 School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450000,China
    2 Songshan Laboratory,Zhengzhou 450000,China
    3 National Digital Switching System Engineering & Technological R&D Center,Zhengzhou 450000,China
  • Received:2024-03-25 Revised:2024-08-16 Online:2025-05-15 Published:2025-05-12
  • About author:LI Xiwang,born in 1999,postgraduate.His main research interests include relation extraction and data mining.
    SHE Wei,born in 1977,Ph.D,professor.His main research interests include complex system modeling and simulation,machine learning and intelligent systems,information security and blockchain technology.
  • Supported by:
    National Key Research and Development Program of China(31703-3) and Songshan Laboratory Pre-research Project(YYYY022022003).

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Abstract: Safety risk management is the core assignment to ensure safety,and the traditional methods of identifying safety risks can no longer meet the needs of intelligent development.Research on relation extraction is of significant importance for security risk management,as it serves as one of the methods for identifying security risks.However,most existing relation extraction models ignore the problem of insufficient representation of domain entity and contain more noise in the data.To address the above problems,a multi-view IB-based safety risk relation extraction model(MIBRE) is proposed.Specifically,it achieves enhanced domain entity semantics by fusing semantic information from multi-view.In order to obtain the maximum relevant information between the two views,an objective function is constructed using the information bottleneck approach.The relevant information is maximally preserved and restored while compressing the information between the two views.Experiments on two real domain datasets show that the F1 value recognized by MIBRE reaches 64.28% and 74.34% respectively,which is 4.41% and 2.98% higher than that of LGGCN based on heterogeneous graph model.Compared with TDGAT based on attention mechanism model,F1 value increased by 1.89% and 1.53% respectively.The effectiveness of the proposed model in security risk identification is verified by experiments.

Key words: Relation extraction, Information bottleneck, Multi-view, Safety risk, Feature fusion

CLC Number: 

  • TP391
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