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| 基于自适应模糊测试的IaaS层漏洞挖掘方法 |
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沙乐天1,2, 肖甫1,2, 杨红柯3, 喻辉4, 王汝传2
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1.南京邮电大学 计算机学院, 江苏 南京 210023;2.江苏省无线传感网高技术重点实验室(南京邮电大学), 江苏 南京 210023;3.华为企业通信技术有限公司 杭州研究所, 浙江 杭州 310052;4.78111部队, 四川 成都 610011
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| 摘要: |
| 云计算在为人们日常生活提供极大便利的同时，也带来了较大的安全威胁.近年来，云平台IaaS层虚拟化机制的漏洞层出不穷，如何有效地挖掘虚拟化实现过程中的拒绝服务及逃逸漏洞，是当前的研究难点.分析已知虚拟化平台的相关漏洞，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可夫模型设计了一种自动化预测方法，以实时地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：这些方法可以有效地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 灰度马尔可夫 |
| DOI：10.13328/j.cnki.jos.005498 |
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| 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重大项目（14KJA520002）；江苏省杰出青年基金（BK20170039） |
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| Vulnerability Discovery Method for Virtualization in IaaS Based on Self-Adapting Fuzzing Test |
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SHA Le-Tian1,2, XIAO Fu1,2, YANG Hong-Ke3, YU Hui4, WANG Ru-Chuan2
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1.School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;2.Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks(Nanjing University of Posts and Telecommunications), Nanjing 210023, China;3.Hangzhou Technology Institute, Huawei Technologies Co. Ltd., Hangzhou 310052, China;4.Army of 78111, Chengdu 610011, China
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| Abstract: |
| Cloud computing provides great convenience for people's daily life,however,it also introduces huge security threats via related technology.Recently more and more vulnerabilities have been discovered for virtualization in IaaS of cloud platform,and 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.In addition,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,called VirtualFuzz,is designed and accomplished in this paper.Experiment data demonstrates DDoS and Escape vulnerabilities can be discovered effectively in the new method.Out of 24 test cases acquired,18 known cases are evaluated and 6 unknown cases are discovered.Moreover,3 vulnerability authentications are obtained by CVE,while the optimized results for efficiency are emphasized via comparison between VirtualFuzz and other Fuzzing tools. |
| Key words: self-adapting fuzzing test gray Markov model |
