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.



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.



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