An OS-agnostic approach to memory forensics

Oliveri, Andrea; Dell'Amico, Matteo; Balzarotti, Davide
NDSS 2023, Network and Distributed System Security Symposium, 27 February-3 March 2023, San Diego, CA, USA

The analysis of memory dumps presents unique challenges, as operating systems use a variety of (often undocumented) ways to represent data in memory. To solve this
problem, forensics tools maintain collections of models that precisely describe the kernel data structures used by a handful of operating systems. However, these models cannot be generalized and developing new models may require a very long and tedious
reverse engineering effort for closed source systems. In the last years, the tremendous increase in the number of IoT devices, smart-home appliances and cloud-hosted VMs resulted in a growing number of OSs which are not supported by current forensics tools. The way we have been doing memory forensics until today, based on handwritten models and rules, cannot simply keep pace with this variety of systems. To overcome this problem, in this paper we introduce the new concept of OS-agnostic memory forensics, which is based on techniques that can recover certain forensics information
without any knowledge of the internals of the underlying OS. Our approach allows to automatically identify different types of data structures by using only their topological constraints and then supports two modes of investigation. In the first, it allows to
traverse the recovered structures by starting from predetermined seeds, i.e., pieces of forensics-relevant information (such as a process name or an IP address) that an analyst knows a priori or that can be easily identified in the dump. Our experiments show
that even a single seed can be sufficient to recover the entire list of processes and other important forensics data structures in dumps obtained from 14 different OSs, without any knowledge of the underlying kernels. In the second mode of operation, our
system requires no seed but instead uses a set of heuristics to rank all memory data structures and present to the analysts only the most ‘promising’ ones. Even in this case, our experiments show that an analyst can use our approach to easily identify forensics-relevant structured information in a truly OS-agnostic scenario.

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