Robust Linux memory acquisition with minimal target impact

Software based Memory acquisition on modern systems typically requires the insertion of a kernel module into the running kernel. On Linux, kernel modules must be compiled against the exact version of kernel headers and the exact kernel configuration used to build the currently executing kernel. This makes Linux memory acquisition significantly more complex in practice, than on other platforms due to the number of variations of kernel versions and configurations, especially when responding to incidents. The Linux kernel maintains a checksum of kernel version and will generally refuse to load a module which was compiled against a different kernel version. Although there are some techniques to override this check, there is an inherent danger leading to an unstable kernel and possible kernel crashes. This paper presents a novel technique to safely load a pre-compiled kernel module for acquisition on a wide range of Linux kernel versions and configuration. Our technique injects a minimal acquisition module (parasite) into another valid kernel module (host) already found on the target system. The resulting combined module is then relinked in such a way as to grant code execution and control over vital data structures to the acquisition code, whilst the host module remains dormant during runtime.     

A survey of main memory acquisition and analysis techniques for the windows operating system

Traditional, persistent data-oriented approaches in computer forensics face some limitations regarding a number of technological developments, e.g., rapidly increasing storage capabilities of hard drives, memory-resident malicious software applications, or the growing use of encryption routines, that make an in-time investigation more and more difficult. In order to cope with these issues, security professionals have started to examine alternative data sources and emphasize the value of volatile system information in RAM more recently. In this paper, we give an overview of the prevailing techniques and methods to collect and analyze a computer's memory. We describe the characteristics, benefits, and drawbacks of the individual solutions and outline opportunities for future research in this evolving field of IT security.     

In‐Depth Analysis of Computer Memory Acquisition Software for Forensic Purposes

The comparison studies on random access memory (RAM) acquisition tools are either limited in metrics or the selected tools were designed to be executed in older operating systems. Therefore, this study evaluates widely used seven shareware or freeware/open source RAM acquisition forensic tools that are compatible to work with the latest 64‐bit Windows operating systems. These tools' user interface capabilities, platform limitations, reporting capabilities, total execution time, shared and proprietary DLLs, modified registry keys, and invoked files during processing were compared. We observed that Windows Memory Reader and Belkasoft's Live Ram Capturer leaves the least fingerprints in memory when loaded. On the other hand, ProDiscover and FTK Imager perform poor in memory usage, processing time, DLL usage, and not‐wanted artifacts introduced to the system. While Belkasoft's Live Ram Capturer is the fastest to obtain an image of the memory, Pro Discover takes the longest time to do the same job.     

Cloud forensics–Tool development studies & future outlook

In this work, we describe our experiences in developing cloud forensics tools and use them to support three main points:First, we make the argument that cloud forensics is a qualitatively different problem. In the context of SaaS, it is incompatible with long-established acquisition and analysis techniques, and requires a new approach and forensic toolset. We show that client-side techniques, which are an extension of methods used over the last three decades, have inherent limitations that can only be overcome by working directly with the interfaces provided by cloud service providers.Second, we present our results in building forensic tools in the form of three case studies: kumodd–a tool for cloud drive acquisition, kumodocs–a tool for Google Docs acquisition and analysis, and kumofs–a tool for remote preview and screening of cloud drive data. We show that these tools, which work with the public and private APIs of the respective services, provide new capabilities that cannot be achieved by examining client-side artifacts.Finally, we use current IT trends, and our lessons learned, to outline the emerging new forensic landscape, and the most likely course of tool development over the next five years.     

When finding nothing may be evidence of something: Anti-forensics and digital tool marks

There are an abundance of measures available to the standard digital device users which provide the opportunity to act in an anti-forensic manner and conceal any potential digital evidence denoting a criminal act. Whilst there is a lack of empirical evidence which evaluates the scale of this threat to digital forensic investigations leaving the true extent of engagement with such tools unknown, arguably the field should take proactive steps to examine and record the capabilities of these measures. Whilst forensic science has long accepted the concept of toolmark analysis as part of criminal investigations, ‘digital tool marks’ (DTMs) are a notion rarely acknowledged and considered in digital investigations. DTMs are the traces left behind by a tool or process on a suspect system which can help to determine what malicious behaviour has occurred on a device. This article discusses and champions the need for DTM research in digital forensics highlighting the benefits of doing so.     

BodySnatcher: Towards reliable volatile memory acquisition by software

Recently there has been a surge in interest in memory forensics: the acquisition and analysis of the contents of physical memory obtained from live hosts. The emergence of kernel level rootkits, anti-forensics, and the threat of subversion that they pose threatens to undermine the reliability of such memory images and digital evidence in general. In this paper we propose a method of acquiring the contents of volatile memory from arbitrary operating systems in a manner that provides point in time atomic snapshots of the host OS volatile memory. Additionally the method is more resistant to subversion due to its reduced attack surface. Our method is to inject an independent, acquisition specific OS into the potentially subverted host OS kernel, snatching full control of the host's hardware. We describe an implementation of this proposal, which we call BodySnatcher, which has demonstrated proof of concept by acquiring memory from Windows 2000 operating systems.     

An evaluation platform for forensic memory acquisition software

Memory forensics has gradually moved into the focus of researchers and practitioners alike in recent years. With an increasing effort to extract valuable information from a snapshot of a computer's RAM, the necessity to properly assess the respective solutions rises as well. In this paper, we present an evaluation platform for forensic memory acquisition software. The platform is capable of measuring distinct factors that determine the quality of a generated memory image, specifically its correctness, atomicity, and integrity. Tests are performed for three popular open source applications, win32dd, WinPMEM, and mdd, as well as for different memory sizes.     

Design and implementation of FROST: Digital forensic tools for the OpenStack cloud computing platform

We describe the design, implementation, and evaluation of FROST—three new forensic tools for the OpenStack cloud platform. Our implementation for the OpenStack cloud platform supports an Infrastructure-as-a-Service (IaaS) cloud and provides trustworthy forensic acquisition of virtual disks, API logs, and guest firewall logs. Unlike traditional acquisition tools, FROST works at the cloud management plane rather than interacting with the operating system inside the guest virtual machines, thereby requiring no trust in the guest machine. We assume trust in the cloud provider, but FROST overcomes non-trivial challenges of remote evidence integrity by storing log data in hash trees and returning evidence with cryptographic hashes. Our tools are user-driven, allowing customers, forensic examiners, and law enforcement to conduct investigations without necessitating interaction with the cloud provider. We demonstrate how FROST's new features enable forensic investigators to obtain forensically-sound data from OpenStack clouds independent of provider interaction. Our preliminary evaluation indicates the ability of our approach to scale in a dynamic cloud environment. The design supports an extensible set of forensic objectives, including the future addition of other data preservation, discovery, real-time monitoring, metrics, auditing, and acquisition capabilities.     

Structure and application of IconCache.db files for digital forensics

Anti-forensics has developed to prevent digital forensic investigations, thus forensic investigations to prevent anti-forensic behaviors have been studied in various area. In the area of user activity analysis, “IconCache.db” files contain icon cache information related to applications, which can yield meaningful information for digital forensic investigations such as the traces of deleted files. A previous study investigated the general artifacts found in the IconCache.db file. In the present study, further features and structures of the IconCache.db file are described. We also propose methods for analyzing anti-forensic behaviors (e.g., time information related to the deletion of files). Finally, we introduce an analytical tool that was developed based on the file structure of IconCache.db. The tool parses out strings from the IconCache.db to assist an analyst. Therefore, an analyst can more easily analyze the IconCache.db file using the tool.     

Dynamic recreation of kernel data structures for live forensics

The role of live forensics in digital forensic investigations has become vital due to the importance of volatile data such as encryption keys, network activity, currently running processes, in memory only malware, and other key pieces of data that are lost when a device is powered down. While the technology to perform the first steps of a live investigation, physical memory collection and preservation, is available, the tools for completing the remaining steps remain incomplete. First-generation memory analyzers performed simple string and regular expression operations on the memory dump to locate data such as passwords, credit card numbers, fragments of chat conversations, and social security numbers. A more in-depth analysis can reveal information such as running processes, networking information, open file data, loaded kernel modules, and other critical information that can be used to gain insight into activity occurring on the machine when a memory acquisition occurred. To be useful, tools for performing this in-depth analysis must support a wide range of operating system versions with minimum configuration. Current live forensics tools are generally limited to a single kernel version, a very restricted set of closely related versions, or require substantial manual intervention.This paper describes techniques developed to allow automatic adaptation of memory analysis tools to a wide range of kernel versions. Dynamic reconstruction of kernel data structures is obtained by analyzing the memory dump for the instructions that reference needed kernel structure members. The ability to dynamically recreate C structures used within the kernel allows for a large amount of information to be obtained and processed. Currently, this capability is used within a tool called RAMPARSER that is able to simulate commands such as ps and netstat as if an investigator were sitting at the machine at the time of the memory acquisition. Other applications of the developed capabilities include kernel-level malware detection, recovery of processes memory and file mappings, and other areas of forensics interest.     

Forensic analysis of newer TomTom devices

Today many investigations involve TomTom devices due to the wide-spread use of these navigation systems. The process of acquiring a memory dump from the first generation of TomTom devices was relatively easy by utilising the USB-connection and standard forensic tools. Newer devices, however, do not provide this or any other readily available data connection, making the task much more complex. In addition to existing and relatively complex chip-extraction procedures, an easier data acquisition method was developed without the need to de-solder flash memory chips. The presence of new files and the differences in data formats found in these devices meant that new methods of data analysis and decoding also needed to be developed.     

Persistent systems techniques in forensic acquisition of memory

In this paper we discuss how operating system design and implementation influence the methodology for computer forensics investigations, with the focus on forensic acquisition of memory. In theory the operating system could support such investigations both in terms of tools for analysis of data and by making the system data readily accessible for analysis. Conventional operating systems such as Windows and UNIX derivatives offer some memory-related tools that are geared towards the analysis of system crashes, rather than forensic investigations. In this paper we demonstrate how techniques developed for persistent operating systems, where lifetime of data is independent of the method of its creation and storage, could support computer forensics investigations delivering higher efficiency and accuracy. It is proposed that some of the features offered by persistent systems could be built into conventional operating systems to make illicit activities easier to identify and analyse. We further propose a new technique for forensically sound acquisition of memory based on the persistence paradigm.     

Modelling and refinement of forensic data acquisition specifications

This paper defines a model of a special type of digital forensics tools, known as data acquisition tools, using the formal refinement language Event-B. The complexity and criticality of many types of computer and Cyber crime nowadays combined with improper or incorrect use of digital forensic tools calls for more robust and reliable specifications of the functionality of digital forensics applications. As a minimum, the evidence produced by such tools must meet the minimum admissibility standards the legal system requires, in general implying that it must be generated from reliable and robust tools. Despite the fact that some research and effort has been spent on the validation of digital forensics tools by means of testing, the verification of such tools and the formal specification of their expected behaviour remains largely under-researched. The goal of this work is to provide a formal specification against which implementations of data acquisition procedures can be analysed.     

Extraction of forensically sensitive information from windows physical memory

Forensic analysis of physical memory is gaining good attention from experts in the community especially after recent development of valuable tools and techniques. Investigators find it very helpful to seize physical memory contents and perform post-incident analysis of this potential evidence. Most of the research carried out focus on enumerating processes and threads by accessing memory resident objects. To collect case-sensitive information from the extracted memory content, the existing techniques usually rely on string matching. The most important contribution of the paper is a new technique for extracting sensitive information from physical memory. The technique is based on analyzing the call stack and the security sensitive APIs. It allows extracting sensitive information that cannot be extracted by string matching-based techniques. In addition, the paper leverages string matching to get a more reliable technique for analyzing and extracting what we called “application/protocol fingerprints”. The proposed techniques and their implementation target the machines running under the Windows XP (SP1, SP2) operating system.     

网络电子证据取证技术与反取证技术研究

随着网络技术的发展,计算机网络犯罪现象日趋严重。为了有效地打击网络犯罪行为,完善网络电子证据立法基础,取证技术专家不仅要研究网络取证相关技术,同时还必须对网络反取证技术充分进行研究。通过研究反取证技术来促进取证技术的提高,这样才能在网络取证过程中拓宽思路,提高获取有效证据的效率。     

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.     

Leveraging virtual machine introspection with memory forensics to detect and characterize unknown malware using machine learning techniques at hypervisor

The Virtual Machine Introspection (VMI) has emerged as a fine-grained, out-of-VM security solution that detects malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS). Specifically, it functions by the Virtual Machine Monitor (VMM), or hypervisor. The reconstructed semantic details obtained by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, the existing out-of-VM security solutions require extensive manual analysis. In this paper, we propose an advanced VMM-based, guest-assisted Automated Internal-and-External (A-IntExt) introspection system by leveraging VMI, Memory Forensics Analysis (MFA), and machine learning techniques at the hypervisor. Further, we use the VMI-based technique to introspect digital artifacts of the live guest OS to obtain a semantic view of the processes details. We implemented an Intelligent Cross View Analyzer (ICVA) and implanted it into our proposed A-IntExt system, which examines the data supplied by the VMI to detect hidden, dead, and dubious processes, while also predicting early symptoms of malware execution on the introspected guest OS in a timely manner. Machine learning techniques are used to analyze the executables that are mined and extracted using MFA-based techniques and ascertain the malicious executables. The practicality of the A-IntExt system is evaluated by executing large real-world malware and benign executables onto the live guest OSs. The evaluation results achieved 99.55% accuracy and 0.004 False Positive Rate (FPR) on the 10-fold cross-validation to detect unknown malware on the generated dataset. Additionally, the proposed system was validated against other benchmarked malware datasets and the A-IntExt system outperforms the detection of real-world malware at the VMM with performance exceeding 6.3%.     

Forensic limbo: Towards subverting hard disk firmware bootkits

We discuss the problem posed by malicious hard disk firmware towards forensic data acquisition. To this end, we analyzed the Western Digital WD3200AAKX model series (16 different drives) in depth and outline methods for detection and subversion of current state of the art bootkits possibly located in these particular hard disks' EEPROMs. We further extend our analysis to a total of 23 different hard drive models (16 HDDs and 7 SSDs) from 10 different vendors and provide a theoretical discussion on how hard disk rootkits residing in the firmware overlays and/or modules stored in the special storage area on a HDD called the Service Area could be detected. To this end, we outline the various debug interfacing possibilities of the various hard disk drives and how they can be used to perform a live analysis of the hard disk controller, such as dumping its memory over JTAG or UART, or how to access the Service Area via vendor specific commands over SATA.     

Scanning memory with Yara

Memory analysis has been successfully utilized to detect malware in many high profile cases. The use of signature scanning to detect malicious tools is becoming an effective triaging and first response technique. In particular, the Yara library and scanner has emerged as the defacto standard in malware signature scanning for files, and there are many open source repositories of yara rules. Previous attempts to incorporate yara scanning in memory analysis yielded mixed results. This paper examines the differences between applying Yara signatures on files and in memory and how yara signatures can be developed to effectively search for malware in memory. For the first time we document a technique to identify the process owner of a physical page using the Windows PFN database. We use this to develop a context aware Yara scanning engine which can scan all processes simultaneously using a single pass over the physical image.     

A reference database of Windows artifacts for file-wiping tool execution analysis

Anti-forensic technology can play an effective role in protecting information, but it can make forensic investigations difficult. Specifically, file-wiping permanently erases evidence, making it challenging for investigators to determine whether a file ever existed and prolonging the investigation process. To address this issue, forensic researchers have studied anti-forensic techniques that detect file-wiping activities. Many previous studies have focused on the effects of file-wiping tools on $MFT, $LogFile, and $DATA, rather than on Windows artifacts. Additionally, previous studies that have examined Windows artifacts have considered different artifacts, making it difficult to study them in a comprehensive manner. To address this, we focused on analyzing traces in 13 Windows artifacts of 10 file-wiping tools' operations in the Windows operating system comprehensively. For our experiments, we installed each file-wiping tool on separate virtual machines and checked the traces that the tools left behind in each artifact. We then organized the results in a database format. Our analysis revealed that most of the tools left traces on other artifacts, except for JumpList, Open&SavePidlMRU, and lnk. There were also some cases where traces remained on the other three artifacts. Based on our research, forensic investigators can quickly identify whether a file-wiping tool has been used, and it can assist in decision-making for evidence collection and forensic triage.