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结合CNN和文本语义的漏洞自动分类方法

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结合CNN和文本语义的漏洞自动分类方法
Automatic Classification of Vulnerabilities Based on CNN and Text Semantics
投稿时间：2018-06-15
DOI：10.15918/j.tbit1001-0645.2019.07.013
中文关键词:卷积神经网络漏洞分类国家信息安全漏洞库
English Keywords:convoputional nered networkvulnerability classificationChina national vulnerability database of information security
基金项目:
作者单位
曲泷玉中国信息安全测评中心, 北京 100085
贾依真中国信息安全测评中心, 北京 100085
郝永乐中国信息安全测评中心, 北京 100085
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中文摘要:
为解决大规模漏洞分类问题，提出一种基于卷积神经网络（convolutional neural network，CNN）的漏洞自动分类方法，借鉴深度学习的技术思想自动获取漏洞描述的相关局部特征，通过batchnorm规范化数据解决文本训练不稳定问题，进而实现漏洞类型的有效划分.实验表明，与传统方法相比，该方法在漏洞自动分类效率上能够得到显著的提高.
English Summary:
Vulnerability classification technology is an important basis in information security vulnerability research, and is also an important means for effective management and control of vulnerability resources. In order to solve the problem of large-scale classification of vulnerabilities, an automatic vulnerability classification method was proposed based on convolutional neural network. Referring to the thought of deep learning, relevant local features of vulnerability description were acquired automatically, and the unstable problem of text training was solved through batchnorm normalized data, so as to realize the effective classification of vulnerabilities. Experiments show that compared with the traditional method, the efficiency of automatic classification of vulnerabilities can be improved to a certain degree with the proposed method.
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