AutoProfile: Towards automated profile generation for memory analysis

Pagani, Fabio; Balzarotti, Davide
ACM Transactions on Privacy and Security (TOPS), Vol.25, N°1, Article N°6, February 2022

Despite a considerable number of approaches that have been proposed to protect computer systems, cyber-criminal activities are on the rise and forensic analysis of compromised machines and seized devices is becoming essential in computer security.

This article focuses on memory forensics, a branch of digital forensics that extract artifacts from the volatile memory. In particular, this article looks at a key ingredient required by memory forensics frameworks: a precise model of the OS kernel under analysis, also known as profile. By using the information stored in the profile, memory forensics tools are able to bridge the semantic gap and interpret raw bytes to extract evidences from a memory dump.

A big problem with profile-based solutions is that custom profiles must be created for each and every system under analysis. This is especially problematic for Linux systems, because profiles are not generic: they are strictly tied to a specific kernel version and to the configuration used to build the kernel. Failing to create a valid profile means that an analyst cannot unleash the true power of memory forensics and is limited to primitive carving strategies.

For this reason, in this article we present a novel approach that combines source code and binary analysis techniques to automatically generate a profile from a memory dump, without relying on any non-public information. Our experiments show that this is a viable solution and that profiles reconstructed by our framework can be used to run many plugins, which are essential for a successful forensics investigation.


DOI
Type:
Journal
Date:
2021-11-23
Department:
Digital Security
Eurecom Ref:
6750
Copyright:
© ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Privacy and Security (TOPS), Vol.25, N°1, Article N°6, February 2022 https://doi.org/10.1145/3485471

PERMALINK : https://www.eurecom.fr/publication/6750