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.     

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.     

Digital Stratigraphy: Contextual Analysis of File System Traces in Forensic Science

This work introduces novel methods for conducting forensic analysis of file allocation traces, collectively called digital stratigraphy. These in‐depth forensic analysis methods can provide insight into the origin, composition, distribution, and time frame of strata within storage media. Using case examples and empirical studies, this paper illuminates the successes, challenges, and limitations of digital stratigraphy. This study also shows how understanding file allocation methods can provide insight into concealment activities and how real‐world computer usage can complicate digital stratigraphy. Furthermore, this work explains how forensic analysts have misinterpreted traces of normal file system behavior as indications of concealment activities. This work raises awareness of the value of taking the overall context into account when analyzing file system traces. This work calls for further research in this area and for forensic tools to provide necessary information for such contextual analysis, such as highlighting mass deletion, mass copying, and potential backdating.     

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.     

An automated timeline reconstruction approach for digital forensic investigations

Existing work on digital forensics timeline generation focuses on extracting times from a disk image into a timeline. Such an approach can produce several million ‘low-level’ events (e.g. a file modification or a Registry key update) for a single disk. This paper proposes a technique that can automatically reconstruct high-level events (e.g. connection of a USB stick) from this set of low-level events. The paper describes a framework that extracts low-level events to a SQLite backing store which is automatically analysed for patterns. The provenance of any high-level events is also preserved, meaning that from a high-level event it is possible to determine the low-level events that caused its inference, and from those, the raw data that caused the low-level event to be initially created can also be viewed. The paper also shows how such high-level events can be visualised using existing tools.     

Triaging digital device content at-scene:- Formalising the decision-making process

The prominence of technology usage in society has inevitably led to increasing numbers of digital devices being seized, where digital evidence often features in criminal investigations. Such demand has led to well documented backlogs placing pressure on digital forensic labs, where in an effort to combat this issue, the ‘at-scene triage’ of devices has been touted as a solution. Yet such triage approaches are not straightforward to implement with multiple technical and procedural issues existing, including determining when it is actually appropriate to triage the contents of a device at-scene. This work remains focused on this point due to the complexities associated with it, and to support first responders a nine-stage triage decision model is offered which is designed to promote consistent and transparent practice when determining if a device should be triaged.     

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.     

Human Identification Using Automatic and Semi‐Automatically Detected Facial Marks

Continuing advancements in the field of digital cameras and surveillance imaging devices have led law enforcement and intelligence agencies to use analysis of images and videos for the investigation and prosecution of crime. When determining identity from photographic evidence, forensic analysts perform comparison of visible facial features manually, which is inefficient. In this study, we will address research efforts to use facial marks as biometric signatures to distinguish between individuals. We propose two systems to assist forensic analysts during photographic comparison: an improved multiscale facial mark system in which facial marks are detected automatically, and a semi‐automatic facial mark system that integrates human knowledge within the improved multiscale facial mark system. Experiment results employ a high‐resolution time‐elapsed dataset acquired at the University of Notre Dame between 2009 and 2011. The results indicate that the geometric distributions of facial mark patterns can be used to distinguish between individuals.     

Digital evidence strategies for digital forensic science examinations

Given the size and complexity of many digital forensic science device examinations, there is a need for practitioners to formally and strategically determine a course of conduct which allows them to undertake the most robust and efficient examination possible. This work outlines both the need for practitioners to have a digital evidence strategy (DES) when tackling any given examination scenario, how to construct one and the concerns which exist when no formal DES is in place. Approaches to DES development are examined and the context to which they should be deployed are analysed, with focus being on the use of DESs at the examination/processing stage of the investigative workflow. Finally, a ‘DES skeleton’ is offered to guide practitioners as they seek to create their own DES.     

Challenges in digital forensics

Abstract

Various terms have been used to describe the intersection between computing technology and violations of the law-including computer crime, electronic crime, and cybercrime. While there remains little agreement on terminology, most experts agree that the use of electronic devices to commit crime has increased dramatically and is now commonplace. It is the role of the digital investigator to bring cybercriminals to justice. Cybercrime however differs from traditional crime and presents a variety of unique challenges including the variety of electronic devices available, amount of data produced by these devices, the absence of standard practices and guidelines for analyzing that data, the lack qualified personnel to perform investigations and the lack of resources to provide on-going training. This paper examines these challenges     

Automated mapping of large binary objects using primitive fragment type classification

Security analysts, reverse engineers, and forensic analysts are regularly faced with large binary objects, such as executable and data files, process memory dumps, disk images and hibernation files, often Gigabytes or larger in size and frequently of unknown, suspect, or poorly documented structure. Binary objects of this magnitude far exceed the capabilities of traditional hex editors and textual command line tools, frustrating analysis. This paper studies automated means to map these large binary objects by classifying regions using a multi-dimensional, information-theoretic approach. We make several contributions including the introduction of the binary mapping metaphor and its associated applications, as well as techniques for type classification of low-level binary fragments. We validate the efficacy of our approach through a series of classification experiments and an analytic case study. Our results indicate that automated mapping can help speed manual and automated analysis activities and can be generalized to incorporate many low-level fragment classification techniques.     

Placing the suspect at a PC: A preliminary study involving fingerprints on keyboards and mice

Digital devices now play an important role in the lives of many in society. Whilst they are used predominantly for legitimate purposes, instances of digital crime are witnessed, where determining their usage is important to any criminal investigation. Typically, when determining who has used a digital device, digital forensic analysis is utilised, however, biological trace evidence or fingerprints residing on its surfaces may also be of value. This work provides a preliminary study which examines the potential for fingerprint recovery from computer peripherals, namely keyboards and mice. Our implementation methodology is outlined, and results discussed which indicate that print recovery is possible. Findings are intended to support those operating at-scene in an evidence collection capacity.     

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.     

“I couldn't find it your honour,it mustn't be there!” – Tool errors,tool limitations and user error in digital forensics

The field of digital forensics maintains significant reliance on the software it uses to acquire and investigate forms of digital evidence. Without these tools, analysis of digital devices would often not be possible. Despite such levels of reliance, techniques for validating digital forensic software are sparse and research is limited in both volume and depth. As practitioners pursue the goal of producing robust evidence, they face the onerous task of both ensuring the accuracy of their tools and, their effective use. Whilst tool errors provide one issue, establishing a tool's limitations also provides an investigatory challenge leading the potential for practitioner user-error and ultimately a grey area of accountability. This article debates the problems surrounding digital forensic tool usage, evidential reliability and validation.     

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.     

Advancing coordinated cyber-investigations and tool interoperability using a community developed specification language

Any investigation can have a digital dimension, often involving information from multiple data sources, organizations and jurisdictions. Existing approaches to representing and exchanging cyber-investigation information are inadequate, particularly when combining data sources from numerous organizations or dealing with large amounts of data from various tools. To conduct investigations effectively, there is a pressing need to harmonize how this information is represented and exchanged. This paper addresses this need for information exchange and tool interoperability with an open community-developed specification language called Cyber-investigation Analysis Standard Expression (CASE). To further promote a common structure, CASE aligns with and extends the Unified Cyber Ontology (UCO) construct, which provides a format for representing information in all cyber domains. This ontology abstracts objects and concepts that are not CASE-specific, so that they can be used across other cyber disciplines that may extend UCO. This work is a rational evolution of the Digital Forensic Analysis eXpression (DFAX) for representing digital forensic information and provenance. CASE is more flexible than DFAX and can be utilized in any context, including criminal, corporate and intelligence. CASE also builds on the Hansken data model developed and implemented by the Netherlands Forensic Institute (NFI). CASE enables the fusion of information from different organizations, data sources, and forensic tools to foster more comprehensive and cohesive analysis. This paper includes illustrative examples of how CASE can be implemented and used to capture information in a structured form to advance sharing, interoperability and analysis in cyber-investigations. In addition to capturing technical details and relationships between objects, CASE provides structure for representing and sharing details about how cyber-information was handled, transferred, processed, analyzed, and interpreted. CASE also supports data marking for sharing information at different levels of trust and classification, and for protecting sensitive and private information. Furthermore, CASE supports the sharing of knowledge related to cyber-investigations, including distinctive patterns of activity/behavior that are common across cases. This paper features a proof-of-concept Application Program Interface (API) to facilitate implementation of CASE in tools. Community members are encouraged to participate in the development and implementation of CASE and UCO.     

A general strategy for differential forensic analysis

The dramatic growth of storage capacity and network bandwidth is making it increasingly difficult for forensic examiners to report what is present on a piece of subject media. Instead, analysts are focusing on what characteristics of the media have changed between two snapshots in time. To date different algorithms have been implemented for performing differential analysis of computer media, memory, digital documents, network traces, and other kinds of digital evidence. This paper presents an abstract differencing strategy and applies it to all of these problem domains. Use of an abstract strategy allows the lessons gleaned in one problem domain to be directly applied to others.     

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.     

Digital evidence in fog computing systems

Fog Computing provides a myriad of potential societal benefits: personalised healthcare, smart cities, automated vehicles, Industry 4.0, to name just a few. The highly dynamic and complex nature of Fog Computing with its low latency communication networks connecting sensors, devices and actuators facilitates ambient computing at scales previously unimaginable. The combination of Machine Learning, Data Mining, and the Internet of Things, supports endless innovation in our data driven society. Fog computing incurs new threats to security and privacy since these become more difficult when there are an increased number of connected devices, and such devices (for example sensors) typically have limited capacity for in-built security. For law enforcement agencies, the existing models for digital forensic investigations are ill suited to the emerging fog paradigm. In this paper we examine the procedural, technical, legal, and geopolitical challenges associated with digital forensic investigations in Fog Computing. We highlight areas that require further development, and posit a framework to stimulate further consideration and discussion around the challenges associated with extracting digital evidence from Fog Computing systems.