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.     

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.     

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.     

Analyzing Data Remnant Remains on User Devices to Determine Probative Artifacts in Cloud Environment

Cloud storage service allows users to store their data online, so that they can remotely access, maintain, manage, and back up data from anywhere via the Internet. Although helpful, this storage creates a challenge to digital forensic investigators and practitioners in collecting, identifying, acquiring, and preserving evidential data. This study proposes an investigation scheme for analyzing data remnants and determining probative artifacts in a cloud environment. Using pCloud as a case study, this research collected the data remnants available on end‐user device storage following the storing, uploading, and accessing of data in the cloud storage. Data remnants are collected from several sources, including client software files, directory listing, prefetch, registry, network PCAP, browser, and memory and link files. Results demonstrate that the collected remnants data are beneficial in determining a sufficient number of artifacts about the investigated cybercrime.     

Impacts of increasing volume of digital forensic data: A survey and future research challenges

A major challenge to digital forensic analysis is the ongoing growth in the volume of data seized and presented for analysis. This is a result of the continuing development of storage technology, including increased storage capacity in consumer devices and cloud storage services, and an increase in the number of devices seized per case. Consequently, this has led to increasing backlogs of evidence awaiting analysis, often many months to years, affecting even the largest digital forensic laboratories. Over the preceding years, there has been a variety of research undertaken in relation to the volume challenge. Solutions posed range from data mining, data reduction, increased processing power, distributed processing, artificial intelligence, and other innovative methods. This paper surveys the published research and the proposed solutions. It is concluded that there remains a need for further research with a focus on real world applicability of a method or methods to address the digital forensic data volume challenge.     

Forensic investigation of cross platform massively multiplayer online games: Minecraft as a case study

Minecraft, a Massively Multiplayer Online Game (MMOG), has reportedly millions of players from different age groups worldwide. With Minecraft being so popular, particularly with younger audiences, it is no surprise that the interactive nature of Minecraft has facilitated the commission of criminal activities such as denial of service attacks against gamers, cyberbullying, swatting, sexual communication, and online child grooming. In this research, there is a simulated scenario of a typical Minecraft setting, using a Linux Ubuntu 16.04.3 machine (acting as the MMOG server) and Windows client devices running Minecraft. Server and client devices are then examined to reveal the type and extent of evidential artefacts that can be extracted.     

Visualizing Digital Forensic Datasets: A Proof of Concept

Digital forensic visualization is an understudied area despite its potential to achieve significant improvements in the efficiency of an investigation, criminal or civil. In this study, a three‐stage forensic data storage and visualization life cycle is presented. The first stage is the decoding of data, which involves preparing both structured and unstructured data for storage. In the storage stage, data are stored within our proposed database schema designed for ensuring data integrity and speed of storage and retrieval. The final stage is the visualization of stored data in a manner that facilitates user interaction. These functionalities are implemented in a proof of concept to demonstrate the utility of the proposed life cycle. The proof of concept demonstrates the utility of the proposed approach for the storage and visualization of digital forensic data.     

BitTorrent Sync: First Impressions and Digital Forensic Implications

With professional and home Internet users becoming increasingly concerned with data protection and privacy, the privacy afforded by popular cloud file synchronisation services, such as Dropbox, OneDrive and Google Drive, is coming under scrutiny in the press. A number of these services have recently been reported as sharing information with governmental security agencies without warrants. BitTorrent Sync is seen as an alternative by many and has gathered over two million users by December 2013 (doubling since the previous month). The service is completely decentralised, offers much of the same synchronisation functionality of cloud powered services and utilises encryption for data transmission (and optionally for remote storage). The importance of understanding BitTorrent Sync and its resulting digital investigative implications for law enforcement and forensic investigators will be paramount to future investigations. This paper outlines the client application, its detected network traffic and identifies artefacts that may be of value as evidence for future digital investigations.     

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.     

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.     

A case study on anonymised sharing platforms and digital traces left by their usage

Non-local forms of file storage and transfer provide investigatory concerns. Whilst mainstream cloud providers offer a well-established challenge to those involved in criminal enquiries, there are also a host of services offering non-account based ‘anonymous’ online temporary file storage and transfer. From the context of a digital forensic investigation, the practitioner examining a suspect device must detect when such services have been utilised by a user, as offending files may not be resident on local storage media. In addition, identifying the use of a service may also expose networks of illegal file distribution, supporting wider investigations into criminal activity. This work examines 16 anonymous file transfer services and identifies and interprets the digital traces left behind on a device following their use to support law enforcement investigations.     

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

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

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.     

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.     

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.     

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.     

Distributed filesystem forensics: XtreemFS as a case study

Distributed filesystems provide a cost-effective means of storing high-volume, velocity and variety information in cloud computing, big data and other contemporary systems. These technologies have the potential to be exploited for illegal purposes, which highlights the need for digital forensic investigations. However, there have been few papers published in the area of distributed filesystem forensics. In this paper, we aim to address this gap in knowledge. Using our previously published cloud forensic framework as the underlying basis, we conduct an in-depth forensic experiment on XtreemFS, a Contrail EU-funded project, as a case study for distributed filesystem forensics. We discuss the technical and process issues regarding collection of evidential data from distributed filesystems, particularly when used in cloud computing environments. A number of digital forensic artefacts are also discussed. We then propose a process for the collection of evidential data from distributed filesystems.     

Digital evidence in cloud computing systems

Cloud computing systems provide a new paradigm to the distributed processing of digital data. Digital forensic investigations involving such systems are likely to involve more complex digital evidence acquisition and analysis. Some public cloud computing systems may involve the storage and processing of digital data in different jurisdictions, and some organisations may choose to encrypt their data before it enters the cloud. Both of these factors in conjunction with cloud architectures may make forensic investigation of such systems more complex and time consuming. There are no established digital forensic guidelines that specifically address the investigation of cloud computing systems. In this paper we examine the legal aspects of digital forensic investigations of cloud computing systems.     

Ideal log setting for database forensics reconstruction

The ability to reconstruct the data stored in a database at an earlier time is an important aspect of database forensics. Past research shows that the log file in a database can be useful for reconstruction. However, in many database systems there are various options that control which information is included in the logs. This paper introduces the notion of the ideal log setting necessary for an effective reconstruction process in database forensics. The paper provides a survey of the default logging preferences in some of the popular database management systems and identifies the information that a database log should contain in order to be useful for reconstruction. The challenges that may be encountered in storing the information as well as ways of overcoming the challenges are discussed. Possible logging preferences that may be considered as the ideal log setting for the popular database systems are also proposed. In addition, the paper relates the identified requirements to the three dimensions of reconstruction in database forensics and points out the additional requirements and/or techniques that may be required in the different dimensions.     

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.