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基于自适应模糊测试的IaaS层漏洞挖掘方法
引用本文:沙乐天,肖甫,杨红柯,喻辉,王汝传.基于自适应模糊测试的IaaS层漏洞挖掘方法[J].软件学报,2018,29(5):1303-1317.
作者姓名:沙乐天  肖甫  杨红柯  喻辉  王汝传
作者单位:南京邮电大学计算机学院, 江苏 南京 210023;江苏省无线传感网高技术重点实验室, 江苏 南京 210023,南京邮电大学计算机学院, 江苏 南京 210023;江苏省无线传感网高技术重点实验室, 江苏 南京 210023,华为企业通信技术有限公司杭州研究所, 浙江 杭州 310052,78111部队, 四川 成都 610011,江苏省无线传感网高技术重点实验室, 江苏 南京 210023
基金项目:国家自然科学基金(61373137,61572260,61702283);江苏省高校自然科学研究计划重大项目(14KJA520002);江苏省杰出青年基金项目(BK20170039)
摘    要:抽取并推演目标数据集合,设计并实现了一种随机化的模糊测试方法,进一步基于灰度马尔科夫模型设计了一种自动化预测方法,实时监督并调整模糊测试的方向,实现面向虚拟化平台的自适应模糊测试目的.最终设计并实现了原型系统VirtualFuzz,实验数据表明:所提方法可有效检测虚拟化平台中的拒绝服务及逃逸漏洞,共得到24个漏洞测试用例,其中验证了18个已知漏洞,挖掘得到了6个未知漏洞,且已有3个漏洞获得CVE授权;同时通过与其他模糊测试工具的对比突出了原型系统的性能优化效果.

关 键 词:自适应|模糊测试|灰度马尔科夫
收稿时间:2017/7/1 0:00:00
修稿时间:2017/8/29 0:00:00

Vulnerability Discovery Method for Virtualization in IaaS Based on Self-Adapting Fuzzing Test
SHA Le-Tian,XIAO Fu,YANG Hong-Ke,YU Hui and WANG Ru-Chuan.Vulnerability Discovery Method for Virtualization in IaaS Based on Self-Adapting Fuzzing Test[J].Journal of Software,2018,29(5):1303-1317.
Authors:SHA Le-Tian  XIAO Fu  YANG Hong-Ke  YU Hui and WANG Ru-Chuan
Institution:School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, China,School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, China,Hangzhou Technology Institute of HUAWEI Company, Hangzhou 310052, China,Army of 78111, Chengdu 610011, China and Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, China
Abstract:It has provided large convenience for data lifetime of people by cloud computing, however, huge security threatens has been introduced via related technology. Recently more and more vulnerabilities have been discovered for virtualization in IaaS of cloud platform, it can be viewed as a difficult problem to discover DDoS and Escape vulnerabilities in virtualization mechanism. In this paper, some known bugs are analyzed for related platforms, target test case sets are extracted and extended, and randomized fuzzing test is designed and accomplished. Finally, an automatic prediction is proposed based on gray Markova model, via which the direction of fuzzing test can be supervised and adjusted in real time, and self-adapting fuzzing test can be achieved for virtualization platform. Finally, a prototype is designed and accomplished in this paper, called VirtualFuzz, as shown in experiment data, DDoS and Escape vulnerabilities can be discovered effectively in our method, 24 test cases are acquired, in which 18 known cases are evaluated and 6 unknown cases are discovered. Moreover, we have gained 3 vulnerability authentications by CVE. In addition, the optimized results for efficiency are emphasized via comparison between VirtualFuzz and other Fuzzing tools.
Keywords:Self-adapting|Fuzzing test|Gray Markov Model
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