BrowStExPlus: A Tool to Aggregate IndexedDB Artifacts for Forensic Analysis

As the Internet and World Wide Web have rapidly evolved and revolutionized the applications in everyday life, it is a demanding challenge for investigators to keep up with the emerging technologies for forensic analyses. Investigating web browser usages for criminal activities, also known as web browser forensics, is a significant part of digital forensics as crucial browsing information of the suspect can be discovered. Particularly, in this study, an emerging web storage technology, called IndexedDB, is examined. Characteristics of IndexedDB technology in five major web browsers under three major operating systems are scrutinized. Also, top 15 US websites ranked by Alexa are investigated for their data storage in IndexedDB. User screen names, ids, and records of conversations, permissions, and image locations are some of the data found in IndexedDB. Furthermore, BrowStEx, a proof‐of‐concept tool previously developed, is extended and cultivated into BrowStExPlus, with which aggregating IndexedDB artifacts is demonstrated.     

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.     

Characteristics of Forensic Imaging Performance—An Analysis of Forensic Imaging Bottlenecks

Disk imaging involves copying all of the data from a source disk drive to a target. Typically, the target for the copy is another disk drive. Forensic processes developed years ago do not appear to be adequate for current storage technology. For example, with disk drive capacities now exceeding 1 Terabyte, a typical disk imaging can take over 8 hours at typical rates. With disk drive capacities increasing, forensic copying is expected to take even longer. Along with increase in disk capacity, the industry has also seen an increase in data transfer rates. In many cases, forensic imaging is taking longer than necessary. To identify the bottlenecks, an examination of different methods used to transfer data from a source disk was performed. Factors considered were differing disk access technologies. One finding is that the USB disk access technology (version 2.0 and earlier) is a significant bottleneck for data transfer rates, especially when the USB device is a write‐blocker. Other factors that contribute to the efficiency of a forensic copy are the file system used to write a forensic image and the data transfer size used when reading from a disk drive. Optimal parameters for performing a forensic acquisition from a disk drive are identified.     

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.     

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.     

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.     

Information assurance in a distributed forensic cluster

When digital forensics started in the mid-1980s most of the software used for analysis came from writing and debugging software. Amongst these tools was the UNIX utility ‘dd’ which was used to create an image of an entire storage device. In the next decade the practice of creating and using ‘an image’ became established as a fundamental base of what we call ‘sound forensic practice’. By virtue of its structure, every file within the media was an integrated part of the image and so we were assured that it was wholesome representation of the digital crime scene. In an age of terabyte media ‘the image’ is becoming increasingly cumbersome to process, simply because of its size. One solution to this lies in the use of distributed systems. However, the data assurance inherent in a single media image file is lost when data is stored in separate files distributed across a system. In this paper we assess current assurance practices and provide some solutions to the need to have assurance within a distributed system.     

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 .     

Bone Mineral Density Adult Age Estimation in Forensic Anthropology: A Test of the DXAGE Application

Estimating age‐at‐death of individuals represented only by skeletonized human remains is a fundamental aspect of forensic anthropological casework. Recently, several researchers have proposed that bone mineral density (BMD) is a useful predictor of age‐at‐death in forensic contexts. Navega et al. (JFS 63(2):497–503) developed an online application called DXAGE for calculating age‐at‐death from BMD parameters. This study tests the utility of DXAGE by utilizing data from the National Health and Nutrition Examination Survey (NHANES). BMD data from a female subsample (n = 470) of the NHANES 2007–2008 dataset were analyzed, and the relationship between predicted age and real age was examined. Inaccuracy was 14.25 years, and bias was ?7.20 years. Results show that there is a weak correlation between predicted and actual age (r = 0.47) using the DXAGE application. While BMD data are potentially useful for predicting age age‐at‐death, the DXAGE application should be used cautiously in forensic anthropological contexts.     

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.     

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.     

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 investigation framework for the document store NoSQL DBMS: MongoDB as a case study

The NoSQL DBMS provides an efficient means of storing and accessing big data because its servers are more easily horizontally scalable and replicable than relational DBMSs. Its data model lacks a fixed schema, so that users can easily dynamically change the data model of applications. These characteristics of the NoSQL DBMS mean that it is increasingly used in real-time analysis, web services such as SNS, mobile apps and the storage of machine generated data such as logs and IoT (Internet of Things) data. Although the increased usage of the NoSQL DBMS increases the possibility of it becoming a target of crime, there are few papers about forensic investigation of NoSQL DBMS.In this paper, we propose a forensic investigation framework for the document store NoSQL DBMS. It is difficult to cover all of the NoSQL DBMS, as 'NoSQL' includes several distinct architectures; our forensic investigation framework, however, is focused on the document store NoSQL DBMS. In order to conduct an evaluative case study, we need to apply it to MongoDB, which is, a widely used document store NoSQL DBMS. For this case study, a crime scenario is created in an experimental environment, and then we propose in detail a forensic procedure and technical methods for MongoDB. We suggested many substantial technical investigation methods for MongoDB, including identifying real servers storing evidences in a distributed environment and transaction reconstruction method, using log analysis and recovering deleted data from the MongoDB data file structure.     

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.     

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.     

Passive forensics in image and video using noise features: A review

Due to present of enormous free image and video editing software on the Internet, tampering of digital images and videos have become very easy. Validating the integrity of images or videos and detecting any attempt of forgery without use of active forensic technique such as Digital Signature or Digital Watermark is a big challenge to researchers. Passive forensic techniques, unlike active techniques, do not need any preembeded information about the image or video. The proposed paper presents a comprehensive review of the recent developments in the field of digital image and video forensic using noise features. The previously existing methods of image and video forensics proved the importance of noises and encourage us for the study and perform extensive research in this field. Moreover, in this paper, forensic task cover mainly source identification and forgery detection in the image and video using noise features. Thus, various source identification and forgery detection methods using noise features are reviewed and compared in this paper for image and video. The overall objective of this paper is to give researchers a broad perspective on various aspects of image and video forensics using noise features. Conclusion part of this paper discusses about the importance of noise features and the challenges encountered by different image and video forensic method using noise features.     

Assessment of Silicone Polymer Composites for Environmental Forensic Applications: A Proof of Concept Study*

Abstract: This study evaluates the use of polydimethylsiloxane polymer composites (PDMS, Fe–PDMS) as a passive sampling media to preconcentrate analytes found in environmental settings. Samplers were made using commercially available silicone products. The composite samplers were assessed for their sorption properties using Atrazine and Irgarol 1051 as model compounds. The initial study assessed the utility of PDMS sheets as adsorption material by following analyte depletion from spiked water samples by solid‐phase microextraction gas chromatography/mass spectrometry (GC/MS). Follow‐up studies conducted at high and low concentrations using lab manufactured iron‐ PDMS rods (Fe–PDMS) showed effective uptake at differential rates from concentrations ranging between 1 μg/L and 10 μg/L. Adsorption mechanism was reversible, and compounds were recovered from the exposed materials and analyzed by liquid–liquid extraction‐GC/MS. Both composites showed better affinity for Irgarol 1051, 100% removal, than for Atrazine, 30% removal, likely representing their K_OW differences, 3.6 and 2.6, respectively. This “proof of concept” study demonstrates the positive implications for the use of silicon polymer composites as a monitoring tool for environmental forensic purposes.     

Cloud forecasting: Legal visibility issues in saturated environments

The advent of cloud computing has brought the computing power of corporate data processing and storage centers to lightweight devices. Software-as-a-service cloud subscribers enjoy the convenience of personal devices along with the power and capability of a service. Using logical as opposed to physical partitions across cloud servers, providers supply flexible and scalable resources. Furthermore, the possibility for multitenant accounts promises considerable freedom when establishing access controls for cloud content. For forensic analysts conducting data acquisition, cloud resources present unique challenges. Inherent properties such as dynamic content, multiple sources, and nonlocal content make it difficult for a standard to be developed for evidence gathering in satisfaction of United States federal evidentiary standards in criminal litigation. Development of such standards, while essential for reliable production of evidence at trial, may not be entirely possible given the guarantees to privacy granted by the Fourth Amendment and the Electronic Communications Privacy Act. Privacy of information on a cloud is complicated because the data is stored on resources owned by a third-party provider, accessible by users of an account group, and monitored according to a service level agreement. This research constructs a balancing test for competing considerations of a forensic investigator acquiring information from a cloud.     

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.     

The Prevalence of Encoded Digital Trace Evidence in the Nonfile Space of Computer Media,,

Forensically significant digital trace evidence that is frequently present in sectors of digital media not associated with allocated or deleted files. Modern digital forensic tools generally do not decompress such data unless a specific file with a recognized file type is first identified, potentially resulting in missed evidence. Email addresses are encoded differently for different file formats. As a result, trace evidence can be categorized as Plain in File (PF), Encoded in File (EF), Plain Not in File (PNF), or Encoded Not in File (ENF). The tool bulk_extractor finds all of these formats, but other forensic tools do not. A study of 961 storage devices purchased on the secondary market and shows that 474 contained encoded email addresses that were not in files (ENF). Different encoding formats are the result of different application programs that processed different kinds of digital trace evidence. Specific encoding formats explored include BASE64, GZIP, PDF, HIBER, and ZIP.