An overall assessment of Mobile Internal Acquisition Tool

The smartphone market provides a great variety of manufacturers and models causing a strong (and mandatory) heterogeneity of the hardware tools adopted to retrieve smartphone contents in a forensically sound way. Thus, in order to lighten the burden for already overtaxed police operators, with possible increase of forensics productivity, we already identified, in a previous work, a new Mobile Equipment (ME) acquisition paradigm. In fact, it's possible to avoid the practical problems related to the USB/mobile phone plug heterogeneity, currently used by the mobile forensics tools, through the use of the MMC/SD slot, part of the hardware equipment of the target ME. This solution overcomes the problems related to the acquisition through the cables, simply relying on a piece of software installed stored into the SD/MMC. The contribution of this paper enriches the methodology already presented by the authors and presents some fundamental properties of the Mobile Internal Acquisition Tool (MIAT) in order to assess the performance with respect to the state of the art of the mobile forensics tools. The results of the assessment encourage the adoption of this tool, since integrity, performances and operational methodology mostly overall benefit from this approach, while, in the worst case, remain at the same level of the state of the art COTS. Finally, this tool, intended to be released under an Open Source license, proposes the paradigm where the acquisition source code is in the public domain, while the analysis and presentation are left to self-made/proprietary tools.     

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.     

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.     

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.     

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.     

A study of user data integrity during acquisition of Android devices

At the time of this writing, Android devices are widely used, and many studies considering methods of forensic acquisition of data from Android devices have been conducted. Similarly, a diverse collection of smartphone forensic tools has also been introduced. However, studies conducted thus far do not normally guarantee data integrity required for digital forensic investigations. Therefore, this work uses a previously proposed method of Android device acquisition utilizing ‘Recovery Mode’. This work evaluates Android Recovery Mode variables that potentially compromise data integrity at the time of data acquisition. Based on the conducted analysis, an Android data acquisition tool that ensures the integrity of acquired data is developed, which is demonstrated in a case study to test tool's ability to preserve data integrity.     

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.     

Android anti-forensics through a local paradigm

Mobile devices are among the most disruptive technologies of the last years, gaining even more diffusion and success in the daily life of a wide range of people categories. Unfortunately, while the number of mobile devices implicated in crime activities is relevant and growing, the capability to perform the forensic analysis of such devices is limited both by technological and methodological problems. In this paper, we focus on Anti-Forensic techniques applied to mobile devices, presenting some fully automated instances of such techniques to Android devices. Furthermore, we tested the effectiveness of such techniques versus both the cursory examination of the device and some acquisition tools.     

Massive threading: Using GPUs to increase the performance of digital forensics tools

The current generation of Graphics Processing Units (GPUs) contains a large number of general purpose processors, in sharp contrast to previous generation designs, where special-purpose hardware units (such as texture and vertex shaders) were commonly used. This fact, combined with the prevalence of multicore general purpose CPUs in modern workstations, suggests that performance-critical software such as digital forensics tools be “massively” threaded to take advantage of all available computational resources.Several trends in digital forensics make the availability of more processing power very important. These trends include a large increase in the average size (measured in bytes) of forensic targets, an increase in the number of digital forensics cases, and the development of “next-generation” tools that require more computational resources. This paper presents the results of a number of experiments that evaluate the effectiveness of offloading processing common to digital forensics tools to a GPU, using “massive” numbers of threads to parallelize the computation. These results are compared to speedups obtainable by simple threading schemes appropriate for multicore CPUs. Our results indicate that in many cases, the use of GPUs can substantially increase the performance of digital forensics tools.     

Cookie信息取证方法简述

本文综述了计算机取证中涉及Cookie取证方面的基础知识和应用技术。分别从Cookie基本常识、Cookie存在的安全方面问题、Cookie获取方法、Cookie相关软件工具和Cookie缓存文件信息获取5个方面进行了综述。     

IntroLib: Efficient and transparent library call introspection for malware forensics

Dynamic malware analysis aims at revealing malware's runtime behavior. To evade analysis, advanced malware is able to detect the underlying analysis tool (e.g. one based on emulation.) On the other hand, existing malware-transparent analysis tools incur significant performance overhead, making them unsuitable for live malware monitoring and forensics. In this paper, we present IntroLib, a practical tool that traces user-level library calls made by malware with low overhead and high transparency. IntroLib is based on hardware virtualization and resides outside of the guest virtual machine where the malware runs. Our evaluation of an IntroLib prototype with 93 real-world malware samples shows that IntroLib is immune to emulation and API hooking detection by malware, uncovers more semantic information about malware behavior than system call tracing, and incurs low overhead (<15% in all-but-one test case) in performance benchmark testing.     

Android智能手机的取证

作为一种新兴的智能手机,Android手机发展势头极为迅猛,并越来越多的受到人们的关注。通过对Android智能手机的取证研究,在介绍了Android手机的基本工作原理后,详细描述了取证方式。通过Android SDK工具对手机内外置存储进行镜像备份,逻辑分析利用文件系统分析,查找每个应用程序自带的数据库文件来获得有价值信息,物理分析通过对内存镜像进行数据恢复以寻找删除的文件,两者互相结合。结果表明,能够从Android手机中有效寻找到潜在证据。     

An Android Communication App Forensic Taxonomy

Due to the popularity of Android devices and applications (apps), Android forensics is one of the most studied topics within mobile forensics. Communication apps, such as instant messaging and Voice over IP (VoIP), are one popular app category used by mobile device users, including criminals. Therefore, a taxonomy outlining artifacts of forensic interest involving the use of Android communication apps will facilitate the timely collection and analysis of evidentiary materials from such apps. In this paper, 30 popular Android communication apps were examined, where a logical extraction of the Android phone images was collected using XRY, a widely used mobile forensic tool. Various information of forensic interest, such as contact lists and chronology of messages, was recovered. Based on the findings, a two‐dimensional taxonomy of the forensic artifacts of the communication apps is proposed, with the app categories in one dimension and the classes of artifacts in the other dimension. Finally, the artifacts identified in the study of the 30 communication apps are summarized using the taxonomy. It is expected that the proposed taxonomy and the forensic findings in this paper will assist forensic investigations involving Android communication apps.     

A RAM triage methodology for Hadoop HDFS forensics

This paper discusses the challenges of performing a forensic investigation against a multi-node Hadoop cluster and proposes a methodology for examiners to use in such situations. The procedure's aim of minimising disruption to the data centre during the acquisition process is achieved through the use of RAM forensics. This affords initial cluster reconnaissance which in turn facilitates targeted data acquisition on the identified DataNodes. To evaluate the methodology's feasibility, a small Hadoop Distributed File System (HDFS) was configured and forensic artefacts simulated upon it by deleting data originally stored in the cluster. RAM acquisition and analysis was then performed on the NameNode in order to test the validity of the suggested methodology. The results are cautiously positive in establishing that RAM analysis of the NameNode can be used to pinpoint the data blocks affected by the attack, allowing a targeted approach to the acquisition of data from the DataNodes, provided that the physical locations can be determined. A full forensic analysis of the DataNodes was beyond the scope of this project.     

The persistence of memory: Forensic identification and extraction of cryptographic keys

The increasing popularity of cryptography poses a great challenge in the field of digital forensics. Digital evidence protected by strong encryption may be impossible to decrypt without the correct key. We propose novel methods for cryptographic key identification and present a new proof of concept tool named Interrogate that searches through volatile memory and recovers cryptographic keys used by the ciphers AES, Serpent and Twofish. By using the tool in a virtual digital crime scene, we simulate and examine the different states of systems where well known and popular cryptosystems are installed. Our experiments show that the chances of uncovering cryptographic keys are high when the digital crime scene are in certain well-defined states. Finally, we argue that the consequence of this and other recent results regarding memory acquisition require that the current practices of digital forensics should be guided towards a more forensically sound way of handling live analysis in a digital crime scene.     

Forensic Inspection of Sensitive User Data and Artifacts from Smartwatch Wearable Devices

Wearable devices allow users the ability to leave mobile phones behind while remaining connected to the digital world; however, this creates challenges in the examination, acquisition, identification, and analysis of probative data. This preliminary research aims to provide an enhanced understanding of where sensitive user data and forensic artifacts are stored on smartwatch wearable devices, both through utilization as a connected and standalone device. It also provides a methodology for the forensically sound acquisition of data from a standalone smartwatch wearable device. The results identify significant amounts of data on the Samsung^? Gear S3 Frontier, greater than that stored on the companion mobile phone. An Apple Watch^® Series 3 manual examination method which produces native screenshots was identified; however, the companion mobile phone was found to store the greatest amount of data. As a result of this research, a data extraction tool for the Samsung^? Gear S3 Frontier was created.     

An Evidence‐based Forensic Taxonomy of Windows Phone Dating Apps

Advances in technologies including development of smartphone features have contributed to the growth of mobile applications, including dating apps. However, online dating services can be misused. To support law enforcement investigations, a forensic taxonomy that provides a systematic classification of forensic artifacts from Windows Phone 8 (WP8) dating apps is presented in this study. The taxonomy has three categories, namely: Apps Categories, Artifacts Categories, and Data Partition Categories. This taxonomy is built based on the findings from a case study of 28 mobile dating apps, using mobile forensic tools. The dating app taxonomy can be used to inform future studies of dating and related apps, such as those from Android and iOS platforms.     

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.     

Taxonomy of Challenges for Digital Forensics

Since its inception, over a decade ago, the field of digital forensics has faced numerous challenges. Despite different researchers and digital forensic practitioners having studied and analysed various known digital forensic challenges, as of 2013, there still exists a need for a formal classification of these challenges. This article therefore reviews existing research literature and highlights the various challenges that digital forensics has faced for the last 10 years. In conducting this research study, however, it was difficult for the authors to review all the existing research literature in the digital forensic domain; hence, sampling and randomization techniques were employed to facilitate the review of the gathered literature. Taxonomy of the various challenges is subsequently proposed in this paper based on our review of the literature. The taxonomy classifies the large number of digital forensic challenges into four well‐defined and easily understood categories. The proposed taxonomy can be useful, for example, in future developments of automated digital forensic tools by explicitly describing processes and procedures that focus on addressing specific challenges identified in this paper. However, it should also be noted that the purpose of this paper was not to propose any solutions to the individual challenges that digital forensics face, but to serve as a survey of the state of the art of the research area.     

BrowStEx: A tool to aggregate browser storage artifacts for forensic analysis

Web storage or browser storage, a new client-side data storage feature, was recommended as a part of the HTML5 specifications and now widely adopted by major web browser vendors. Web storage with native browser support has changed the paradigm of web application development unprecedentedly because persistent data storage with increased data size can be realized on the client. Web storage is poised to quickly become an area of particular interest for forensic investigators due to the potential to discover critical information from web browser artifacts at client side. However, the literature work on web browser forensics has traditionally focused on browsing history, browser cache, and cookie files (Oh et al., 2011). Therefore, we first discuss the prevalence of web storage implementation in widely used websites. Then, we compare and contrast the web storage technology currently implemented in the five major web browsers, Google Chrome, Internet Explorer, Mozilla Firefox, Opera, and Apple's Safari. Moreover, in order to provide more insights into web storage and enable unified forensic analysis, a proof-of-concept tool, named as BrowStEx (Browser Storage Extractor), is described with implementation details. The commonalities, differences, and the proof-of-concept tool discussed in this paper can be useful in developing advanced forensic tools that can extract browser storage artifacts.