SCARF: A container-based approach to cloud-scale digital forensic processing

Abstract

The rapid growth of raw data volume requiring forensic processing has become one of the top concerns of forensic analysts. At present, there are no readily available solutions that provide: a) open and flexible integration of existing forensic tools into a processing pipeline; and b) scale-out architecture that is compatible with common cloud technologies. Containers, lightweight OS-level virtualized environments, are quickly becoming the preferred architectural unit for building large-scale data processing systems. We present a container-based software framework, SCARF, which applies this approach to forensic computations. Our prototype demonstrates its practicality by providing low-cost integration of both custom code and a variety of third-party tools via simple data interfaces. The resulting system fits well with the data parallel nature of most forensic tasks, which tend to have few dependencies that limit parallel execution. Our experimental evaluation shows that for several types of processing tasks–such as hashing, indexing and bulk processing–performance scales almost linearly with the addition of hardware resources. We show that the software engineering effort to integrate new tools is quite modest, and all the critical task scheduling and resource allocation are automatically managed by the container orchestration runtime–Docker Swarm, or similar.