Automatic Classification of Vulnerabilities Based on CNN and Text Semantics

被引：0
|
作者：
Qu L.-Y. [1 ]
Jia Y.-Z. [1 ]
Hao Y.-L. [1 ]
机构：
[1] China Information Technology Security Evaluation Center, Beijing
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2019年 / 39卷 / 07期
关键词：
China national vulnerability database of information security; Convoputional nered network; Vulnerability classification;
D O I：
10.15918/j.tbit1001-0645.2019.07.013
中图分类号：
学科分类号：
摘要：
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. © 2019, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
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页码：738 / 742
页数：4
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