首页 | 本学科首页   官方微博 | 高级检索  
     

面向项目版本差异性的漏洞识别技术研究
作者姓名:黄诚  孙明旭  段仁语  吴苏晟  陈斌
作者单位:1. 四川大学网络空间安全学院,四川 成都 610065;2. 广西密码学与信息安全重点实验室,广西 桂林541000
基金项目:国家自然科学基金(61902265);四川省科技厅重点研发项目(2020YFG0047);广西密码学与信息安全重点实验室研究课题(GCIS201921)
摘    要:开源代码托管平台为软件开发行业带来了活力和机遇,但存在诸多安全隐患.开源代码的不规范性、项目依赖库的复杂性、漏洞披露平台收集漏洞的被动性等问题都影响着开源项目及引入开源组件的闭源项目的 安全,大部分漏洞修复行为无法及时被察觉和识别,进而将各类项目的 安全风险直接暴露给攻击者.为了全面且及时地发现开源项目中的漏洞修复行为...

关 键 词:漏洞识别  开源平台  安全修复  机器学习

Vulnerability identification technology research based on project version difference
Authors:Cheng HUANG  Mingxu SUN  Renyu DUAN  Susheng WU  Bin CHEN
Affiliation:1. School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China;2. Guangxi Key Laboratory of Cryptography and Information Security, Guilin 541000, China
Abstract:The open source code hosting platform has brought power and opportunities to software development, but there are also many security risks.The open source code has poor quality, the dependency libraries of projects are complex and vulnerability collection platforms are inadequate in collecting vulnerabilities.All these problems affect the security of open source projects and complex software with open source complements and most security patches can't be discovered and applied in time.Thus, the hackers could be easily found such vulnerable software.To discover the vulnerability in the open source community fully and timely, a vulnerability identification system based on project version difference was proposed.The update contents of projects in the open source community were collected automatically, then features were defined as security behaviors and code differences from the code and log in patches, 40 features including comment information feature group, page statistics feature group, code statistics feature group and vulnerability type feature group were proposed to build feature set.And random forest model was built to learn classifiers for vulnerability identification.The results show that VpatchFinder achieves a precision rate of 0.844, an accuracy rate of 0.855 and a recall rate of 0.851.Besides, 68.07% of community vulnerabilities can be early discovered by VpatchFinder in real open source CVE vulnerabilities.This research result can improve the current issue in software security architecture design and development.
Keywords:vulnerability detection  open source platform  security patch  machine learning  
点击此处可从《》浏览原始摘要信息
点击此处可从《》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号