Title :
Multi-Aspect, Robust, and Memory Exclusive Guest OS Fingerprinting
Author :
Yufei Gu ; Yangchun Fu ; Prakash, Aravind ; Zhiqiang Lin ; Heng Yin
Author_Institution :
Dept. of Comput. Sci., Univ. of Texas at Dallas, Richardson, TX, USA
Abstract :
Precise fingerprinting of an operating system (OS) is critical to many security and forensics applications in the cloud, such as virtual machine (VM) introspection, penetration testing, guest OS administration, kernel dump analysis, and memory forensics. The existing OS fingerprinting techniques primarily inspect network packets or CPU states, and they all fall short in precision and usability. As the physical memory of a VM always exists in all these applications, in this article, we present OS-SOMMELIER+, a multi-aspect, memory exclusive approach for precise and robust guest OS fingerprinting in the cloud. It works as follows: given a physical memory dump of a guest OS, OS-SOMMELIER+ first uses a code hash based approach from kernel code aspect to determine the guest OS version. If code hash approach fails, OS-SOMMELIER+ then uses a kernel data signature based approach from kernel data aspect to determine the version. We have implemented a prototype system, and tested it with a number of Linux kernels. Our evaluation results show that the code hash approach is faster but can only fingerprint the known kernels, and data signature approach complements the code signature approach and can fingerprint even unknown kernels.
Keywords :
Linux; cloud computing; digital forensics; digital signatures; Linux kernels; OS-SOMMELIER; code hash based approach; code signature approach; kernel code aspect; kernel data signature; memory exclusive guest OS fingerprinting; multiaspect memory exclusive approach; operating system precise fingerprinting; physical memory dump; Cloud computing; Computer security; Data structures; Fingerprint recognition; Forensics; Linux; Virtual machining; Operating system fingerprinting; memory forensics; virtual machine introspection;
Journal_Title :
Cloud Computing, IEEE Transactions on
DOI :
10.1109/TCC.2014.2338305