摘要
针对漏洞文本分类任务中的多类型分类问题和文本语义与标签语义利用不充分的问题,提出一种基于知识增强与标签语义(knowledge enhancement and labeling semantics, KELS)的多标签漏洞文本分类方法。首先,面向多种漏洞类型,引入漏洞属性知识,提出异构信息有效整合的策略,提升漏洞文本表示对多标签关系的表达能力,通过Transformer编码器的深度融合,进一步实现知识对文本语义的增强。其次,引入标签嵌入,提出一种融合知识增强文本表示与标签嵌入的标签注意力机制,用以捕捉文本与标签的语义关联,进行漏洞分类。实验结果表明,对比基线模型,KELS模型在Micro-P、Micro-F1、Macro-P、Macro-F1分别提高了0.98%、0.32%、2.31%、0.75%,有效提升了多标签漏洞分类的准确性。
Aiming at the multi-type classification problem and the underutilization of text semantics and labeling semantics in the vulnerability text classification task,a multi-label vulnerability classification method based on knowledge enhancement and labeling semantics(KELS)was proposed.Firstly,vulnerability attribute knowledge was introduced for multiple vulnerability types,and a strategy for effective integration of heterogeneous information was proposed to enhance the expressive ability of vulnerability text representation for multi-tag relationship,and further realize the enhancement of knowledge on text semantics through the deep fusion of Transformer encoder.Secondly,label embedding was introduced to propose a label attention mechanism that fuses knowledge-enhanced text representation with label embedding to capture semantic associations between text and labels for vulnerability classification.The experimental results show that comparing with the baseline model,the KELS model improves 0.98%,0.32%,2.31%,and 0.75%in Micro-P,Micro-F 1,Macro-P,and Macro-F 1,respectively,which effectively improves the accuracy of multi-label vulnerability classification.
作者
边晓云
赵刚
BIAN Xiao-yun;ZHAO Gang(Computer School,Beijing Information Science&Technology University,Beijing 102206,China;The Research Center of Business Intelligence,Beijing 102206,China;Management Science and Engineering,Beijing Information Science&Technology University,Beijing 102206,China)
出处
《科学技术与工程》
北大核心
2026年第1期262-269,共8页
Science Technology and Engineering
基金
国家重点研发计划(2019YFB1405003)。
关键词
漏洞分类
知识增强
标签语义
多标签文本分类
特征融合
vulnerability classification
knowledge enhancement
label semantics
multi-label text classification
feature fusion
