人工智能安全知识图谱构建技术研究

doi:10.11959/j.issn.2096-109x.2023030

Abstract

Abstract:

As a major strategic technology, artificial intelligence is developing rapidly while bringing numerous security risks.Currently, security data for artificial intelligence is collected from disparate sources and lacks standardized description, making it difficult to integrate and analyze effectively.To address this issue, a method for constructing an artificial intelligence security knowledge graph was proposed.The knowledge graph was used to integrate the current multi-source heterogeneous data, scientifically represent complex relationships of the data, mine potential value and form a domain knowledge base.In view of the diversity and correlation of concepts in the field of artificial intelligence security, a hierarchical structure of artificial intelligence security ontology was proposed to make the ontology structure more diversified and extensible, provide rule constraints for the process of knowledge graph construction, and form an artificial intelligence security knowledge base.To effectively utilize feature information and reduce noise interference, named entity recognition algorithm based on BiLSTM-CRF and relationship extraction algorithm based on CNN-ATT were adopted for information extraction.The constructed artificial intelligence security dataset was then used to verify the performance of the algorithm.Based on the proposed ontology, the multi-level visualization results of the artificial intelligence security knowledge graph were presented in 3D effect, effectively connecting the multi-source security data information.The experimental results show that the constructed knowledge graph meets the multi-dimensional evaluation criteria of accuracy, consistency, completeness, and timeliness, providing knowledge support for artificial intelligence security research.Overall, the proposed method can help address the complexity and heterogeneity of security data in artificial intelligence and provide a more standardized, integrated approach to knowledge representation and analysis.

Key words: artificial intelligence security, knowledge graph, ontology construction, information extraction, visualization

CLC Number:

TP391

Xiaochen SHEN, Yinhui GE, Bo CHEN, Ling YU. Research on construction technology of artificial intelligence security knowledge graph[J]. Chinese Journal of Network and Information Security, 2023, 9(2): 164-174.

Figures/Tables 13

References 20

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实体类型	描述
威胁主体	攻击活动发起者，可以为个人、团体或组织
目标客体	攻击目标资产，包括设备、数据等
攻击活动	针对目标客体的一系列攻击行为
攻击工具	威胁主体在攻击活动中常用的工具，包括防御响应中所使用的工具
攻击模式	威胁主体试图攻击目标客体的方式，如SQL注入等
恶意程序	未经授权或者破坏系统的代码，目的为破坏相关数据、程序或者其他资产的机密性、完整性和可用性
漏洞	威胁主体或目标客体容易遭受威胁的特征
攻击结果	攻击活动的影响

实体类型	所属层次	高频实体实例	实体数目/个
攻击活动	原理层	后门攻击、投毒攻击等	157
目标客体	原理层	训练数据、目标模型等	989
威胁	问题层	模型投毒、数据破坏等	148
防御措施	措施层	模型繁华、噪声混淆等	321
安全专业术语	-	鲁棒性、可解释性等	1 056

模型	准确率	召回率	F1值
CRF	0.838 1	0.834 3	0.833 0
BiLSTM	0.831 0	0.824 2	0.823 1
BiLSTM-CRF	0.864 8	0.849 9	0.851 8

数据集	准确率	召回率	F1值
AISKE	0.943 9	0.538 4	0.684 7

Research on construction technology of artificial intelligence security knowledge graph

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