IFVD: Design of Intelligent Fusion Framework for Vulnerability Data Based on Text Measures

Rui-Yi Li, Shichong Tan, Chensi Wu, Xudong Cao, Haitao He, Wenjie Wang
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Abstract

Security vulnerability database research is an essential part of information security research. With the sharp increase in the number of vulnerabilities in recent years, it has become increasingly important to collect and organize information about existing vulnerability databases. However, there is heterogeneity and redundancy of data between the databases, which makes it challenging to share vulnerability information. In response to the above problems, a comprehensive security vulnerability collection model is proposed. A total of 1.005 million pieces of vulnerability data are collected and analyzed in 11 mainstream vulnerability databases. By introducing the idea of the collection of automation, problems such as the untimely update of vulnerability database information and the low efficiency of vulnerability collection, are solved effectively. The structural framework and functional modules of the automatic vulnerability collection system are introduced, and the specific implementation of the system is given. Based on the text processing technology, the rules of deduplication (95.6% accuracy) and the intelligent framework for vulnerability database (IFVD) are proposed and implemented. Finally, Experiments show the feasibility of the scheme.
基于文本度量的漏洞数据智能融合框架设计
安全漏洞数据库研究是信息安全研究的重要组成部分。近年来,随着漏洞数量的急剧增加,收集和整理现有漏洞数据库的信息变得越来越重要。然而,数据库之间存在数据的异构性和冗余性,这给漏洞信息的共享带来了挑战。针对上述问题,提出了一种综合的安全漏洞收集模型。在11个主流漏洞数据库中,共收集并分析了105万条漏洞数据。通过引入自动化收集的思想,有效地解决了漏洞数据库信息更新不及时、漏洞收集效率低等问题。介绍了漏洞自动收集系统的结构框架和功能模块,给出了系统的具体实现。基于文本处理技术,提出并实现了重复数据删除规则(准确率95.6%)和漏洞库智能框架(IFVD)。最后,通过实验验证了该方案的可行性。
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