嵌入域知识的图形神经网络用于网络威胁情报实体和关系挖掘
Gan Liu1, Kai Lu1,2, Saiqi Pi3
1School of Cyberspace Security (School of Cryptology), Hainan University, Haikou, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究引入了一种用于识别网络威胁及其关系的新模型,改进了网络安全分析. CtiErRe模型通过整合领域知识和先进的人工智能技术来提高威胁情报,以提高准确性.
科学领域:
- 网络安全和人工智能技术
- 用于威胁情报的自然语言处理
背景情况:
- 网络攻击的频率和严重性正在增加,这给网络空间安全带来了重大挑战.
- 命名实体识别 (NER) 有助于识别网络威胁情报中的威胁实体和关系.
- 现有的NER模型在语义表示,实体细分化和关系提取准确性方面扎.
研究的目的:
- 提出一个新的模型,CtiErRe,用于网络威胁实体识别和关系提取.
- 为了更精确的网络威胁情报分析,重新定义网络威胁实体和关系.
- 解决当前模型中关于语义信息,实体细分度和关系传播错误的局限性.
主要方法:
- 使用收集的域名知识构建了一个域名知识图.
- 使用图形卷积网络 (GCN) 嵌入知识图,以丰富威胁情报文本功能.
- 融合GCN嵌入具有使用Layernorm的变压器 (BERT) 双向编码器表示的特征.
- 利用GlobalPointer算法生成威胁实体和关系矩阵进行识别.
主要成果:
- 拟议的CtiErRe模型与现有模型相比显示出更高的性能.
- 在威胁实体识别中实现了92.13%的准确性和93.11%的F1得分.
- 在关系提取中获得了91.45%的准确性和92.45%的F1得分.
结论:
- CtiErRe模型有效地提高了网络威胁实体及其关系的识别.
- 域知识图和先进的NLP技术的整合显著改善了网络威胁情报分析.
- 该模型为提高网络安全防御和分析的效率和准确性提供了一个强大的解决方案.
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