基于大数据技术的软件安全漏洞自动挖掘方法研究  

Research on Automatic Mining Method of Software Security Vulnerabilities Based on Big Data Technology

作　　者：丁俊[1] DING Jun(Anhui Technical College of Industry and Economy,Hefei 230051,China)

机构地区：[1]安徽工业经济职业技术学院,安徽合肥230051

出　　处：《太原师范学院学报（自然科学版）》2022年第1期45-50,共6页Journal of Taiyuan Normal University:Natural Science Edition

基　　金：2019年安徽省高校自然科学研究重点项目(KJ2019A1049);2020年度省级质量工程项目精品课程(2020kfkc130);2020年安徽省质量工程项目《云计算技术与应用工匠工坊模式示范实习实训中心》(2020sxzx04)。

摘　　要：为了提高软件安全漏洞挖掘能力,文章提出了基于大数据技术的软件安全漏洞自动挖掘方法.构建用于软件安全漏洞检测的原始信息采集模型,采用动态信息融合方法进行安全漏洞信息融合.依据信息融合结果,通过统计特征分析方法实现对软件安全漏洞信息的定位分析,结合频谱分布特性来确定安全漏洞的交叉信息分布集,采用大数据技术构建软件安全漏洞信息特征提取模型,并结合多元交叉分布式辨识和大数据技术,构建大数据挖掘的优化模型,从而实现对软件安全漏洞的自适应挖掘.仿真结果表明,该方法的软件安全漏洞特征提取能力较强,说明采用该方法进行软件安全漏洞挖掘的准确性较好.In order to improve the ability of software security vulnerability mining,propose an automatic software security vulnerability mining method based on big data technology.The original information collection model for software security vulnerability detection is constructed,and the dynamic information fusion method is used to fuse the security vulnerability information.According to the results of information fusion,the location analysis of software security vulnerability information is realized by statistical feature analysis method,and the cross information distribution set of security vulnerability is determined by combining the spectrum distribution characteristics.The feature extraction model of software security vulnerability information is constructed by using big data technology,and the optimization model of big data mining is constructed by combining multiple cross distributed identification and big data technology,So as to realize the adaptive mining of software security vulnerabilities.The simulation results show that the method has strong ability to extract features of software security vulnerabilities,which shows that the accuracy of this method is better.

关 键 词：大数据技术 软件安全 安全漏洞 自动挖掘 特征提取 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]