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.     

Generating a Corpus of Mobile Forensic Images for Masquerading user Experimentation

The Periodic Mobile Forensics (PMF) system investigates user behavior on mobile devices. It applies forensic techniques to an enterprise mobile infrastructure, utilizing an on‐device agent named TractorBeam. The agent collects changed storage locations for later acquisition, reconstruction, and analysis. TractorBeam provides its data to an enterprise infrastructure that consists of a cloud‐based queuing service, relational database, and analytical framework for running forensic processes. During a 3‐month experiment with Purdue University, TractorBeam was utilized in a simulated operational setting across 34 users to evaluate techniques to identify masquerading users (i.e., users other than the intended device user). The research team surmises that all masqueraders are undesirable to an enterprise, even when a masquerader lacks malicious intent. The PMF system reconstructed 821 forensic images, extracted one million audit events, and accurately detected masqueraders. Evaluation revealed that developed methods reduced storage requirements 50‐fold. This paper describes the PMF architecture, performance of TractorBeam throughout the protocol, and results of the masquerading user analysis.     

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.     

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.     

Forensic acquisition and analysis of palm webOS on mobile devices

The emergence of webOS on Palm devices has created new challenges and opportunities for digital investigators. With the purchase of Palm by Hewlett Packard, there are plans to use webOS on an increasing number and variety of computer systems. These devices can store substantial amounts of information relevant to an investigation, including digital photographs, videos, call logs, SMS/MMS messages, e-mail, remnants of Web browsing and much more. Although some files can be obtained from such devices with relative ease, the majority of information of forensic interest is stored in databases on a system partition that many mobile forensic tools do not acquire. This paper provides a methodology for acquiring and examining forensic duplicates of user and system partitions from a device running webOS. The primary sources of digital evidence on these devices are covered with illustrative examples. In addition, the recovery of deleted items from various areas on webOS devices is discussed.     

We are meeting on Microsoft Teams: Forensic analysis in Windows,Android, and iOS operating systems

Microsoft released a new communication platform, Microsoft Teams, in 2017. Due in part to COVID-19, the popularity of communication platforms, like Microsoft Teams, increased exponentially. Given its user base and increased popularity, it seems likely that digital forensic investigators will encounter cases where Microsoft Teams is a relevant component. However, because Microsoft Teams is a relatively new application, there is limited forensic research on the application particularly focusing on mobile operating systems. To address this gap, an analysis of data stored at rest by Microsoft Teams was conducted on the Windows 10 operating system as well as on Android and Apple iOS mobile operating systems. Basic functionalities, such as messaging, sharing files, participating in video conferences, and other functionalities that Teams provides, were performed in an isolated testing environment. Cellebrite UFED Physical Analyzer and Magnet AXIOM Examine tools were used to analyze the mobile devices and the Windows device, respectively. Manual or non-automated investigation recovered, at least partially, the majority of artifacts across all three operating systems. In this study, a total of 77.6% of the populated artifacts were partially or fully recovered in the manual investigation. On the other hand, forensic tools used did not automatically recover many of the artifacts found with the manual investigation. Only 13.8% of artifacts were partially or fully recovered by the forensic tools across all three devices. These discovered artifacts and the results of the investigations are presented in order to aid digital forensic investigations.     

Forensic analysis of video file formats

Video file format standards define only a limited number of mandatory features and leave room for interpretation. Design decisions of device manufacturers and software vendors are thus a fruitful resource for forensic video authentication. This paper explores AVI and MP4-like video streams of mobile phones and digital cameras in detail. We use customized parsers to extract all file format structures of videos from overall 19 digital camera models, 14 mobile phone models, and 6 video editing toolboxes. We report considerable differences in the choice of container formats, audio and video compression algorithms, acquisition parameters, and internal file structure. In combination, such characteristics can help to authenticate digital video files in forensic settings by distinguishing between original and post-processed videos, verifying the purported source of a file, or identifying the true acquisition device model or the processing software used for video processing.     

手机物证检验及其在刑事侦查中的应用

随着移动通信技术的迅速发展和广泛应用,手机内部包含的信息已经成为犯罪侦查重要的线索和证据来源。采用专门的符合物证鉴定原理要求的技术方法检验手机的SIM卡存储器、主板存储器和闪存卡,可以获得大量的手机使用者个人信息、通信内容信息、通信发生信息、使用者写入存储信息和手机设置信息等大量信息资料。手机检验结果给出的这些信息具有非常高的侦查和证据价值的,手机也因此成为物证鉴定领域内一个新的检验对象。     

A Forensic Exploration of the Microsoft Windows 10 Timeline

The Microsoft Windows operating system continues to dominate the desktop computing market. With such high levels of usage comes an inferred likelihood of digital forensic practitioners encountering this platform during their investigations. As part of any forensic examination of a digital device, operating system artifacts, which support the identification and understanding of how a user has behaved on their system provide a potential source of evidence. Now, following Microsoft's April 2018 build 1803 release with its incorporated “Timeline” feature, the potential for identifying and tracking user activity has increased. This work provides a timely examination of the Windows 10 Timeline feature demonstrating the ability to recover activity‐based content from within its stored database log files. Examination results and underpinning experimental methodologies are offered, demonstrating the ability to recover activity tile and process information in conjunction with the Windows Timeline. Further, an SQL query has been provided to support the interpretation of data stored within the ActivitiesCache.db .     

Identification of Mobile Phone and Analysis of Original Version of Videos through a Delay Time Analysis of Sound Signals from Mobile Phone Videos

This study designs a method of identifying the camera model used to take videos that are distributed through mobile phones and determines the original version of the mobile phone video for use as legal evidence. For this analysis, an experiment was conducted to find the unique characteristics of each mobile phone. The videos recorded by mobile phones were analyzed to establish the delay time of sound signals, and the differences between the delay times of sound signals for different mobile phones were traced by classifying their characteristics. Furthermore, the sound input signals for mobile phone videos used as legal evidence were analyzed to ascertain whether they have the unique characteristics of the original version. The objective of this study was to find a method for validating the use of mobile phone videos as legal evidence using mobile phones through differences in the delay times of sound input signals.     

Loose‐Lipped Mobile Device Intelligent Personal Assistants: A Discussion of Information Gleaned from Siri on Locked iOS Devices

The forensic analysis of mobile handsets is becoming a more prominent factor in many criminal investigations. Despite such devices frequently storing relevant evidential content to support an investigation, accessing this information is becoming an increasingly difficult task due to enhanced effective security features. Where access to a device's resident data is not possible via traditional mobile forensic methods, in some cases it may still be possible to extract user information via queries made to an installed intelligent personal assistant. This article presents an evaluation of the information which is retrievable from Apple's Siri when interacted with on a locked iOS device running iOS 11.2.5 (the latest at the time of testing). The testing of verbal commands designed to elicit a response from Siri demonstrate the ability to recover call log, SMS, Contacts, Apple Maps, Calendar, and device information which may support any further investigation.     

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.     

Forensic Investigation of Cooperative Storage Cloud Service: Symform as a Case Study

Researchers envisioned Storage as a Service (StaaS) as an effective solution to the distributed management of digital data. Cooperative storage cloud forensic is relatively new and is an under‐explored area of research. Using Symform as a case study, we seek to determine the data remnants from the use of cooperative cloud storage services. In particular, we consider both mobile devices and personal computers running various popular operating systems, namely Windows 8.1, Mac OS X Mavericks 10.9.5, Ubuntu 14.04.1 LTS, iOS 7.1.2, and Android KitKat 4.4.4. Potential artefacts recovered during the research include data relating to the installation and uninstallation of the cloud applications, log‐in to and log‐out from Symform account using the client application, file synchronization as well as their time stamp information. This research contributes to an in‐depth understanding of the types of terrestrial artifacts that are likely to remain after the use of cooperative storage cloud on client devices.     

USB Storage Device Forensics for Windows 10

Significantly increased use of USB devices due to their user‐friendliness and large storage capacities poses various threats for many users/companies in terms of data theft that becomes easier due to their efficient mobility. Investigations for such data theft activities would require gathering critical digital information capable of recovering digital forensics artifacts like date, time, and device information. This research gathers three sets of registry and logs data: first, before insertion; second, during insertion; and the third, after removal of a USB device. These sets are analyzed to gather evidentiary information from Registry and Windows Event log that helps in tracking a USB device. This research furthers the prior research on earlier versions of Microsoft Windows and compares it with latest Windows 10 system. Comparison of Windows 8 and Windows 10 does not show much difference except for new subkey under USB Key in registry. However, comparison of Windows 7 with latest version indicates significant variances.     

Investigating the uses of mobile phone evidence in China criminal proceedings

Data from mobile phones are regularly used in the investigation of crime and court proceedings. Previously published research has primarily addressed technical issues or provided operational manuals for using forensic science evidence, rather than analysing human factors and the implementation of forensic tools in investigation settings. Moreover, previous research has focused almost entirely on western countries, and there is a dearth of research into the uses of forensic evidence in China. In this study, a review was carried out of court sentencing documents referring to mobile phone evidence in China over the period 2013–2018. Automated content analysis was used to identify the specific evidence types utilised and the sentencing outcome for each case. Results show that mobile phone evidence was used in 3.3% of criminal proceedings. Among various data types mentioned in criminal proceedings, call records sustained as the most frequently used type of data. After which, instant messaging tools (e.g. WeChat) are an increasing proportion of all mobile phone evidence, from 1% in 2015 to 25% in 2018. For cases that utilised mobile phone data, the analysis of instant messaging and online transaction tools is routine, with little variation in the use of each application (WeChat, Alipay, QQ) for investigations of different types of crime. However, in the majority of criminal cases, mobile phone data function as subsidiary evidence and posed limited impacts on verdict reached. The current findings indicate that a large amount of mobile phone evidence was transformed into other evidence formats or filtered out directly before court proceedings.     

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.     

Forensic Taxonomy of Android Social Apps

An Android social app taxonomy incorporating artifacts that are of forensic interest will enable users and forensic investigators to identify the personally identifiable information (PII) stored by the apps. In this study, 30 popular Android social apps were examined. Artifacts of forensic interest (e.g., contacts lists, chronology of messages, and timestamp of an added contact) were recovered. In addition, images were located, and Facebook token strings used to tie account identities and gain access to information entered into Facebook by a user were identified. Based on the findings, a two‐dimensional taxonomy of the forensic artifacts of the social apps is proposed. A comparative summary of existing forensic taxonomies of different categories of Android apps, designed to facilitate timely collection and analysis of evidentiary materials from Android devices, is presented.     

The COLLECTORS ranking scale for ‘at‐scene’ digital device triage

As digital evidence now features prominently in many criminal investigations, such large volumes of requests for the forensic examination of devices has led to well publicized backlogs and delays. In an effort to cope, triage policies are frequently implemented in order to reduce the number of digital devices which are seized unnecessarily. Often first responders are tasked with performing triage at scene in order to decide whether any identified devices should be seized and submitted for forensic examination. In some cases, this is done with the assistance of software which allows device content to be “previewed”; however, in some cases, a first responder will triage devices using their judgment and experience alone, absent of knowledge of the devices content, referred to as “decision‐based device triage” (DBDT). This work provides a discussion of the challenges first responders face when carrying out DBDT at scene. In response, the COLLECTORS ranking scale is proposed to help first responders carry out DBDT and to formalize this process in an effort to support quality control of this practice. The COLLECTORS ranking scale consists of 10 categories which first responders should rank a given device against. Each devices cumulative score should be queried against the defined “seizure thresholds” which offer support to first responders in assessing when to seize a device. To offer clarify, an example use‐case involving the COLLECTORS ranking scale is included, highlighting its application when faced with multiple digital devices at scene.     

Remote Wiping and Secure Deletion on Mobile Devices: A Review

Mobile devices have become ubiquitous in almost every sector of both private and commercial endeavors. As a result of such widespread use in everyday life, many users knowingly and unknowingly save significant amounts of personal and/or commercial data on these mobile devices. Thus, loss of mobile devices through accident or theft can expose users—and their businesses—to significant personal and corporate cost. To mitigate this data leakage issue, remote wiping features have been introduced to modern mobile devices. Given the destructive nature of such a feature, however, it may be subject to criminal exploitation (e.g., a criminal exploiting one or more vulnerabilities to issue a remote wiping command to the victim's device). To obtain a better understanding of remote wiping, we survey the literature, focusing on existing approaches to secure flash storage deletion and provide a critical analysis and comparison of a variety of published research in this area. In support of our analysis, we further provide prototype experimental results for three Android devices, thus providing both a theoretical and applied focus to this article as well as providing directions for further research.     

Windows Surface RT tablet forensics

Small scale digital device forensics is particularly critical as a result of the mobility of these devices, leading to closer proximity to crimes as they occur when compared to computers. The Windows Surface tablet is one such device, combining tablet mobility with familiar Microsoft Windows productivity tools. This research considers the acquisition and forensic analysis of the Windows Surface RT tablet. We discuss the artifacts of both the Windows RT operating system and third-party applications. The contribution of this research is to provide a road map for the digital forensic examination of Windows Surface RT tablets.