Abstract. Examining and modifying data of interest in the memory of a target program is an important capability for security applications such as memory forensics, rootkit detection, game hacking, and virtual machine introspection. In this paper we present a novel memory graph based approach for program data introspection and modification, which does not require source code, debugging symbols, or any API in the target program. It takes as input a sequence of memory snapshots taken while the program executes, and produces a path signature, which can be used in different executions of the program to efficiently locate and traverse the in-memory data structures where the data of interest is stored. We have implemented our approach in a tool called SIGPATH. We have applied SIG-PATH to game hacking, building cheats for 10 popular real-time and turn-based games, and for memory forensics, recovering from snapshots the contacts a user has stored in four IM applications including Skype and Yahoo Messenger.

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 þ , 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 þ first uses a code hash based approach from kernel code aspect to determine the guest OS version. If code hash approach fails, OS-SOMMELIER þ 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.

Modern OS kernels including Windows, Linux, and Mac OS all have adopted kernel Address Space Layout Randomization (ASLR), which shifts the base address of kernel code and data into different locations in different runs. Consequently, when performing introspection or forensic analysis of kernel memory, we cannot use any pre-determined addresses to interpret the kernel events. Instead, we must derandomize the address space layout and use the new addresses. However, few efforts have been made to derandomize the kernel address space and yet there are many questions left such as which approach is more efficient and robust. Therefore, we present the first systematic study of how to derandomize a kernel when given a memory snapshot of a running kernel instance. Unlike the derandomization approaches used in traditional memory exploits in which only remote access is available, with introspection and forensics applications, we can use all the information available in kernel memory to generate signatures and derandomize the ASLR. In other words, there exists a large volume of solutions for this problem. As such, in this paper we examine a number of typical approaches to generate strong signatures from both kernel code and data based on the insight of how kernel code and data is updated, and compare them from efficiency (in terms of simplicity, speed etc.) and robustness (e.g., whether the approach is hard to be evaded or forged) perspective. In particular, we have designed four approaches including brute-force code scanning, patched code signature generation, unpatched code signature generation, and read-only pointer based approach, according to the intrinsic behavior of kernel code and data with respect to kernel ASLR. We have gained encouraging results for each of these approaches and the corresponding experimental results are reported in this paper.

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Precise fingerprinting of an operating system (OS) is critical to many security and virtual machine (VM) management applications in the cloud, such as VM introspection, penetration testing, guest OS administration (e.g., kernel update), 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 is always present in all these applications, in this paper, we present OS-SOMMELIER, a memory-only approach for precise and efficient cloud guest OS fingerprinting. Given a physical memory dump of a guest OS, the key idea of OS-SOMMELIER is to compute the kernel code hash for the precise fingerprinting. To achieve this goal, we face two major challenges: (1) how to differentiate the main kernel code from the rest of code and data in the physical memory, and (2) how to normalize the kernel code to deal with practical issues such as address space layout randomization. We have designed and implemented a prototype system to address these challenges. Our experimental results with over 45 OS kernels, including Linux, Windows, FreeBSD, OpenBSD and NetBSD, show that our OS-SOMMELIER can precisely fingerprint all the tested OSes without any false positives or false negatives, and do so within only 2 seconds on average.