ezswap accommodates not only anonymous pages, but also clean file-mapped pages. To overcome the aforementioned problems and maximize the memory efficiency, we propose a compressed swap scheme, called enhanced zswap (ezswap), for mobile devices.
Last but not least, most of the current compressed swap schemes blindly follow the least-recently-used (LRU) discipline when choosing the victim pages for replacement, not considering the compression ratio or data density of the cached pages. However, our observations revealed that, in mobile devices, file-mapped pages consume significantly more memory than anonymous pages. In addition, they focus only on anonymous pages and not on file-mapped pages, because the latter are backed by on-disk files. Considering that both energy and computing power are scarce resources in mobile devices, and modern applications frequently deal with already-compressed multimedia data, this blind approach may cause adverse impacts. However, most of the current compressed swap schemes indiscriminately compress and store all swap-out pages. The limited memory capacity of mobile devices leads to the popular use of compressed swap schemes, which reduce the I/O operations involving the swapping in and out of infrequently accessed pages.
Through incorporation of these artifacts into analysis, memory analysis frameworks can present an even richer set of artifacts and data to investigators than ever before. macOS tracks pages in a number of different states using a set of queues and as we will illustrate, the reconstruction of data from these queues allows a significant number of memory pages to be analyzed that are currently ignored by memory forensics tools. In this paper, we present the results of our analysis of the macOS page queues subsystem. This leads to more artifacts being reconstructed and made available to an investigator. The more thoroughly the page handling mechanisms are modeled in memory forensics tools, the more pages can be scrutinized during memory analysis. Given its critical role in memory analysis, there has been significant interest in studying the operating system mechanisms responsible for allocating and managing physical pages so that they can be accurately modeled by memory analysis frameworks. Commonly referred to as address translation, this task requires a thorough understanding of the memory management mechanisms of the hardware architecture and operating system version of the device from which the memory sample was acquired.
#Memory monitor 2 osx code
A core task of these frameworks is the discovery and reordering of non-contiguous physical pages in a memory sample into the ordered virtual address spaces used by the operating system and running processes to organize their code and data. To analyze memory samples, an investigator can use one of several available memory analysis frameworks, which are responsible for parsing and presenting the raw data in a meaningful way. Memory forensics has become mainstream in recent years because it allows recovery of a wide variety of artifacts that are never written to the file system and are therefore not available when performing traditional filesystem forensics. Memory forensics is the examination of volatile memory (RAM) for artifacts related to a digital investigation. This will require tool researchers (developers) to spend more time on code documentation and preferably develop plugins instead of stand-alone tools. In order to enhance the status quo, one recommendation is a centralized repository specifically for tested tools. Only 33 of these tools were found to be publicly available, the majority of these were not maintained after development. For this paper we analyzed almost 800 articles from pertinent venues from 2014 to 2019 to answer the following three questions (1) what tools (i.e., in which domains of digital forensics): have been released (2) are they still available, maintained, and documented and (3) are there possibilities to enhance the status quo? We found 62 different tools which we categorized according to digital forensics subfields. However, there has been no study on the tools to understand better what is available and what is missing. These tools are often released to the public for others to reproduce results or use them for their own purposes. Publications in the digital forensics domain frequently come with tools – a small piece of functional software.
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