Reverse engineering of ReFS

File system forensics is an important part of Digital Forensics. Investigators of storage media have traditionally focused on the most commonly used file systems such as NTFS, FAT, ExFAT, Ext2-4, HFS+, APFS, etc. NTFS is the current file system used by Windows for the system volume, but this may change in the future. In this paper we will show the structure of the Resilient File System (ReFS), which has been available since Windows Server 2012 and Windows 8. The main purpose of ReFS is to be used on storage spaces in server systems, but it can also be used in Windows 8 or newer. Although ReFS is not the current standard file system in Windows, while users have the option to create ReFS file systems, digital forensic investigators need to investigate the file systems identified on a seized media. Further, we will focus on remnants of non-allocated metadata structures or attributes. This may allow metadata carving, which means searching for specific attributes that are not allocated. Attributes found can then be used for file recovery. ReFS uses superblocks and checkpoints in addition to a VBR, which is different from other Windows file systems. If the partition is reformatted with another file system, the backup superblocks can be used for partition recovery. Further, it is possible to search for checkpoints in order to recover both metadata and content.Another concept not seen for Windows file systems, is the sharing of blocks. When a file is copied, both the original and the new file will share the same content blocks. If the user changes the copy, new data runs will be created for the modified content, but unchanged blocks remain shared. This may impact file carving, because part of the blocks previously used by a deleted file might still be in use by another file. The large default cluster size, 64 KiB, in ReFS v1.2 is an advantage when carving for deleted files, since most deleted files are less than 64 KiB and therefore only use a single cluster. For ReFS v3.2 this advantage has decreased because the standard cluster size is 4 KiB.Preliminary support for ReFS v1.2 has been available in EnCase 7 and 8, but the implementation has not been documented or peer-reviewed. The same is true for Paragon Software, which recently added ReFS support to their forensic product. Our work documents how ReFS v1.2 and ReFS v3.2 are structured at an abstraction level that allows digital forensic investigation of this new file system. At the time of writing this paper, Paragon Software is the only digital forensic tool that supports ReFS v3.x.It is the most recent version of the ReFS file system that is most relevant for digital forensics, as Windows automatically updates the file system to the latest version on mount. This is why we have included information about ReFS v3.2. However, it is possible to change a registry value to avoid updating. The latest ReFS version observed is 3.4, but the information presented about 3.2 is still valid. In any criminal case, the investigator needs to investigate the file system version found.     

IoT forensics: Exploiting unexplored log records from the HIKVISION file system

CCTV surveillance systems are IoT products that can be found almost everywhere. Their digital forensic analysis often plays a key role in solving crimes. However, it is common for these devices to use proprietary file systems, which frequently hinders a complete examination. HIKVISION is a well-known manufacturer of such devices that typically ships its products with its proprietary file system. The HIKVISION file system has been analyzed before but that research has focused on the recovery of video footage. In this paper, the HIKVISION file system is being revisited regarding the log records it stores. More specifically, these log records are thoroughly examined to uncover both their structure and meaning. These unexplored pieces of evidence remain unexploited by major commercial forensic software, yet they can contain critical information for an investigation. To further assist digital forensic examiners with their analysis, a Python utility, namely the Hikvision Log Analyzer, was developed as part of this study that can automate part of the process.     

A novel file carving algorithm for National Marine Electronics Association (NMEA) logs in GPS forensics

Globe positioning system (GPS) devices are an increasing importance source of evidence, as more of our devices have built-in GPS capabilities. In this paper, we propose a novel framework to efficiently recover National Marine Electronics Association (NMEA) logs and reconstruct GPS trajectories. Unlike existing approaches that require file system metadata, our proposed algorithm is designed based on the file carving technique without relying on system metadata. By understanding the characteristics and intrinsic structure of trajectory data in NMEA logs, we demonstrate how to pinpoint all data blocks belonging to the NMEA logs from the acquired forensic image of GPS device. Then, a discriminator is presented to determine whether two data blocks can be merged. And based on the discriminator, we design a reassembly algorithm to re-order and merge the obtained data blocks into new logs. In this context, deleted trajectories can be reconstructed by analyzing the recovered logs. Empirical experiments demonstrate that our proposed algorithm performs well when the system metadata is available/unavailable, log files are heavily fragmented, one or more parts of the log files are overwritten, and for different file systems of variable cluster sizes.     

Using the HFS+ journal for deleted file recovery

This paper describes research and analysis that were performed to identify a robust and accurate method for identifying and extracting the residual contents of deleted files stored within an HFS+ file system. A survey performed during 2005 of existing tools and techniques for HFS+ deleted file recovery reinforced the need for newer, more accurate techniques.Our research and analysis were based on the premise that a transactional history of file I/O operations is maintained in a Journal on HFS+ file systems, and that this history could be used to reconstruct recent deletions of active files from the file system. Such an approach offered a distinct advantage over other current techniques, including recovery of free/unallocated blocks and “file carving” techniques. If the journal entries contained or referenced file attributes such as the extents that specify which file system blocks were occupied by each file, then a much more accurate identification and recovery of deleted file data would be possible.     

Using the HFS+ journal for deleted file recovery

This paper describes research and analysis that were performed to identify a robust and accurate method for identifying and extracting the residual contents of deleted files stored within an HFS+ file system. A survey performed during 2005 of existing tools and techniques for HFS+ deleted file recovery reinforced the need for newer, more accurate techniques.Our research and analysis were based on the premise that a transactional history of file I/O operations is maintained in a Journal on HFS+ file systems, and that this history could be used to reconstruct recent deletions of active files from the file system. Such an approach offered a distinct advantage over other current techniques, including recovery of free/unallocated blocks and “file carving” techniques. If the journal entries contained or referenced file attributes such as the extents that specify which file system blocks were occupied by each file, then a much more accurate identification and recovery of deleted file data would be possible.     

A comparison of forensic evidence recovery techniques for a windows mobile smart phone

Acquisition, decoding and presentation of information from mobile devices is complex and challenging. Device memory is usually integrated into the device, making isolation prior to recovery difficult. In addition, manufacturers have adopted a variety of file systems and formats complicating decoding and presentation.A variety of tools and methods have been developed (both commercially and in the open source community) to assist mobile forensics investigators. However, it is unclear to what extent these tools can present a complete view of the information held on a mobile device, or the extent the results produced by different tools are consistent.This paper investigates what information held on a Windows Mobile smart phone can be recovered using several different approaches to acquisition and decoding. The paper demonstrates that no one technique recovers all information of potential forensic interest from a Windows Mobile device; and that in some cases the information recovered is conflicting.     

The Linux FAT32 allocator and file creation order reconstruction

The allocation algorithm of the Linux FAT32 file system driver positions files on disk in such a way that their relative positions reveal information on the order in which these files have been created. This provides an opportunity to enrich information from (carved) file fragments with time information, even when such file fragments lack the file system metadata in which time-related information is usually to be found.Through source code analysis and experiments the behaviour of the Linux FAT allocator is examined. How an understanding of this allocator can be applied in practice is demonstrated with a case study involving a TomTom GPS car navigation device. In this case, time information played a crucial role. Large amounts of location records could be carved from this device's flash storage, yielding insight into the locations the device has visited—yet the carved records themselves offered no information on when the device had been at the locations. Still, bounds on the records' time of creation could be inferred when making use of filesystem timestamps related to neighbouring on-disk positions.Finally, we perform experiments which contrast the Linux behaviour with that of Windows 7. We show that the latter differs subtly, breaking the strong relation between creation order and position.     

Automated identification of installed malicious Android applications

Increasingly, Android smartphones are becoming more pervasive within the government and industry, despite the limited ways to detect malicious applications installed to these phones' operating systems. Although enterprise security mechanisms are being developed for use on Android devices, these methods cannot detect previously unknown malicious applications. As more sensitive enterprise information becomes available and accessible on these smartphones, the risk of data loss inherently increases. A malicious application's actions could potentially leave sensitive data exposed with little recourse. Without an effective corporate monitoring solution in place for these mobile devices, organizations will continue to lack the ability to determine when a compromise has occurred. This paper presents research that applies traditional digital forensic techniques to remotely monitor and audit Android smartphones. The smartphone sends changed file system data to a remote server, allowing for expensive forensic processing and the offline application of traditional tools and techniques rarely applied to the mobile environment. The research aims at ascertaining new ways of identifying malicious Android applications and ultimately attempts to improve the state of enterprise smartphone monitoring. An on-phone client, server, database, and analysis framework was developed and tested using real mobile malware. The results are promising that the developed detection techniques identify changes to important system partitions; recognize file system changes, including file deletions; and find persistence and triggering mechanisms in newly installed applications. It is believed that these detection techniques should be performed by enterprises to identify malicious applications affecting their phone infrastructure.     

Visualization in testing a volatile memory forensic tool

We have developed a tool to extract the contents of volatile memory of Apple Macs running recent versions of OS X, which has not been possible since OS X 10.4. This paper recounts our efforts to test the tool and introduces two visualization techniques for that purpose. We also introduce four metrics for evaluating physical memory imagers: correctness, completeness, speed, and the amount of “interference” an imager makes to the state of the machine. We evaluate our tool by these metrics and then show visualization using dotplots, a technique borrowed from bioinformatics, can be used to reveal bugs in the implementation and to evaluate correctness, completeness, and the amount of interference an imager has. We also introduce a visualization we call the density plot which shows the density of repeated pages at various addresses within an image. We use these techniques to evaluate our own tool, Apple’s earlier tools, and compare physical memory images to the hibernation file.     

案卷排他与看得见的程序作用

案卷排他原则,是指行政主体作出行政行为只能以案卷为根据,不能在案卷以外,以当事人所未知悉和未论证的事实为根据。不能把案卷排他原则等同于听证笔录排他原则。案卷排他由最初的单方证据加偏见的排他发展到单方证据排他;由正式程序裁决发展到非正式程序裁决;由英美法系发展到大陆法系。其普适性在于它使程序的作用得以显现,从而保障程序中认定的事实因素与法律因素为作出行政行为的惟一依据,从而使程序的功能和价值得以实现。     

MEGA: A tool for Mac OS X operating system and application forensics

Computer forensic tools for Apple Mac hardware have traditionally focused on low-level file system details. Mac OS X and common applications on the Mac platform provide an abundance of information about the user's activities in configuration files, caches, and logs. We are developing MEGA, an extensible tool suite for the analysis of files on Mac OS X disk images. MEGA provides simple access to Spotlight metadata maintained by the operating system, yielding efficient file content search and exposing metadata such as digital camera make and model. It can also help investigators to assess FileVault encrypted home directories. MEGA support tools are under development to interpret files written by common Mac OS applications such as Safari, Mail, and iTunes.     

MEGA: A tool for Mac OS X operating system and application forensics

Computer forensic tools for Apple Mac hardware have traditionally focused on low-level file system details. Mac OS X and common applications on the Mac platform provide an abundance of information about the user's activities in configuration files, caches, and logs. We are developing MEGA, an extensible tool suite for the analysis of files on Mac OS X disk images. MEGA provides simple access to Spotlight metadata maintained by the operating system, yielding efficient file content search and exposing metadata such as digital camera make and model. It can also help investigators to assess FileVault encrypted home directories. MEGA support tools are under development to interpret files written by common Mac OS applications such as Safari, Mail, and iTunes.     

Can computer forensic tools be trusted in digital investigations?

This paper investigates whether computer forensic tools (CFTs) can extract complete and credible digital evidence from digital crime scenes in the presence of file system anti-forensic (AF) attacks. The study uses a well-established six stage forensic tool testing methodology based on black-box testing principles to carry out experiments that evaluate four leading CFTs for their potential to combat eleven different file system AF attacks. Results suggest that only a few AF attacks are identified by all the evaluated CFTs, while as most of the attacks considered by the study go unnoticed. These AF attacks exploit basic file system features, can be executed using simple tools, and even attack CFTs to accomplish their task. These results imply that evidences collected by CFTs in digital investigations are not complete and credible in the presence of AF attacks. The study suggests that practitioners and academicians should not absolutely rely on CFTs for evidence extraction from a digital crime scene, highlights the implications of doing so, and makes many recommendations in this regard. The study also points towards immediate and aggressive research efforts that are required in the area of computer forensics to address the pitfalls of CFTs.     

Reverse engineering a CCTV system,a case study

Given a disk image of a CCTV system with a non-standard file system, how is the data interpreted? Work has been done in the past detailing the reverse engineering of proprietary file systems and on the process of recovering data from CCTV systems. However, if given a disk image without the CCTV system itself, or if under time constraints, the task becomes much more difficult. This paper explains a different approach to recovering the data and how to make sense of data on a CCTV disk. The method does not require extensive reverse engineering of the CCTV system, or even to have access to the CCTV system itself.     

The windows IconCache.db: A resource for forensic artifacts from USB connectable devices

This paper investigates the evidential potential of the IconCache database file when tracking activity from USB connectable devices on Windows systems. It focuses on the artifacts which are created and retained on a Windows host when executable files are either present on or run from a USB connectable device. Artifacts left in the IconCache database as a result of running executables from a DVD drive or the host itself, are also examined.It is shown that the IconCache.db stores numerous artifacts of investigative interest. These are created on system boot and added to, both when using host-based executables and when installing or using executables from other media. Executables present on USB devices, whether invoked or not, will create artifacts in the IconCache.db. file. Findings should therefore be interpreted carefully and corroborated against other evidence.     

Automated event and social network extraction from digital evidence sources with ontological mapping

The sharp rise in consumer computing, electronic and mobile devices and data volumes has resulted in increased workloads for digital forensic investigators and analysts. The number of crimes involving electronic devices is increasing, as is the amount of data for each job. This is becoming unscaleable and alternate methods to reduce the time trained analysts spend on each job are necessary.This work leverages standardised knowledge representations techniques and automated rule-based systems to encapsulate expert knowledge for forensic data. The implementation of this research can provide high-level analysis based on low-level digital artefacts in a way that allows an understanding of what decisions support the facts. Analysts can quickly make determinations as to which artefacts warrant further investigation and create high level case data without manually creating it from the low-level artefacts. Extraction and understanding of users and social networks and translating the state of file systems to sequences of events are the first uses for this work.A major goal of this work is to automatically derive ‘events’ from the base forensic artefacts. Events may be system events, representing logins, start-ups, shutdowns, or user events, such as web browsing, sending email. The same information fusion and homogenisation techniques are used to reconstruct social networks. There can be numerous social network data sources on a single computer; internet cache can locate Facebook, LinkedIn, Google Plus caches; email has address books and copies of emails sent and received; instant messenger has friend lists and call histories. Fusing these into a single graph allows a more complete, less fractured view for an investigator.Both event creation and social network creation are expected to assist investigator-led triage and other fast forensic analysis situations.     

File fragment encoding classification—An empirical approach

Over the past decade, a substantial effort has been put into developing methods to classify file fragments. Throughout, it has been an article of faith that data fragments, such as disk blocks, can be attributed to different file types. This work is an attempt to critically examine the underlying assumptions and compare them to empirically collected data. Specifically, we focus most of our effort on surveying several common compressed data formats, and show that the simplistic conceptual framework of prior work is at odds with the realities of actual data. We introduce a new tool, zsniff, which allows us to analyze deflate-encoded data, and we use it to perform an empirical survey of deflate-coded text, images, and executables. The results offer a conceptually new type of classification capabilities that cannot be achieved by other means.     

Consensus on validation of forensic voice comparison

Since the 1960s, there have been calls for forensic voice comparison to be empirically validated under casework conditions. Since around 2000, there have been an increasing number of researchers and practitioners who conduct forensic-voice-comparison research and casework within the likelihood-ratio framework. In recent years, this community of researchers and practitioners has made substantial progress toward validation under casework conditions becoming a standard part of practice: Procedures for conducting validation have been developed, along with graphics and metrics for representing the results, and an increasing number of papers are being published that include empirical validation of forensic-voice-comparison systems under conditions reflecting casework conditions. An outstanding question, however, is: In the context of a case, given the results of an empirical validation of a forensic-voice-comparison system, how can one decide whether the system is good enough for its output to be used in court? This paper provides a statement of consensus developed in response to this question. Contributors included individuals who had knowledge and experience of validating forensic-voice-comparison systems in research and/or casework contexts, and individuals who had actually presented validation results to courts. They also included individuals who could bring a legal perspective on these matters, and individuals with knowledge and experience of validation in forensic science more broadly. We provide recommendations on what practitioners should do when conducting evaluations and validations, and what they should present to the court. Although our focus is explicitly on forensic voice comparison, we hope that this contribution will be of interest to an audience concerned with validation in forensic science more broadly. Although not written specifically for a legal audience, we hope that this contribution will still be of interest to lawyers.     

Forensic access to Windows Mobile pim.vol and other Embedded Database (EDB) volumes

Forensic examination of Windows Mobile devices and devices running its successor Windows Phone 7 remains relevant for the digital forensic community. In these devices, the file pim.vol is a Microsoft Embedded Database (EDB) volume that contains information related to contacts, appointments, call history, speed-dial settings and tasks. Current literature shows that analysis of the pim.vol file is less than optimal. We succeeded in reverse-engineering significant parts of the EDB volume format and this article presents our current understanding of the format. In addition we provide a mapping from internal column identifiers to human readable application-level property names for the pim.vol database. We implemented a parser and compared our results to the traditional approach using an emulator and the API provided by the Windows CE operating system. We were able to recover additional databases, additional properties per record and unallocated records.     

Improved recovery and reconstruction of DEFLATEd files

This paper presents a method for recovering data from files compressed with the DEFLATE algorithm where short segments in the middle of the file have been corrupted, yielding a mix of literal bytes, bytes aligned with literals across the corrupted segment, and co-indexed unknown bytes. An improved reconstruction algorithm based on long byte n-grams increases the proportion of reconstructed bytes by an average of 8.9% absolute across the 21 languages of the Europarl corpus compared to previously-published work, and the proportion of unknown bytes correctly reconstructed by an average of 20.9% absolute, while running in one-twelfth the time on average. Combined with the new recovery method, corrupted segments of 128–4096 bytes in the compressed bit-stream result in reconstructed output which differs from the original file by an average of less than twice the number of bytes represented by the corrupted segment. Both new algorithms are implemented in the trainable open-source ZipRec 1.0 utility program.