# EviPlant: An Efficient Digital Forensic Challenge Creation, Manipulation, and Distribution Solution

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Digital Investigation 20 (2017) S29eS36

Contents lists available at ScienceDirect

Digital Investigation
journal homepage: www.elsevier.com/locate/diin

DFRWS 2017 Europe d Proceedings of the Fourth Annual DFRWS Europe

EviPlant: An efficient digital forensic challenge creation, manipulation
and distribution solution
Mark Scanlon*, Xiaoyu Du, David Lillis
School of Computer Science, University College Dublin, Ireland

a r t i c l e i n f o

a b s t r a c t

Article history:
Received 26 January 2017
Accepted 26 January 2017

Education and training in digital forensics requires a variety of suitable challenge corpora containing
realistic features including regular wear-and-tear, background noise, and the actual digital traces to be
discovered during investigation. Typically, the creation of these challenges requires overly arduous effort
on the part of the educator to ensure their viability. Once created, the challenge image needs to be stored
and distributed to a class for practical training. This storage and distribution step requires significant time
and resources and may not even be possible in an online/distance learning scenario due to the data sizes
involved. As part of this paper, we introduce a more capable methodology and system as an alternative to
current approaches. EviPlant is a system designed for the efficient creation, manipulation, storage and
distribution of challenges for digital forensics education and training. The system relies on the initial
distribution of base disk images, i.e., images containing solely base operating systems. In order to create
challenges for students, educators can boot the base system, emulate the desired activity and perform a
“diffing” of resultant image and the base image. This diffing process extracts the modified artefacts and
associated metadata and stores them in an “evidence package”. Evidence packages can be created for
different personae, different wear-and-tear, different emulated crimes, etc., and multiple evidence
packages can be distributed to students and integrated into the base images. A number of additional
applications in digital forensic challenge creation for tool testing and validation, proficiency testing, and
malware analysis are also discussed as a result of using EviPlant.
© 2017 The Author(s). Published by Elsevier Ltd on behalf of DFRWS. This is an open access article under
the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords:
Digital forensics education
Forensic corpora
Digital forensic challenges
Evidence injection
Tool testing and validation

Introduction
Digital forensic investigators are expected to handle the acquisition of digital evidence from an ever-increasing range of devices,
requiring skills originating from a number of different disciplines
including law, statistics, governmental policy, psychology, library
science, and finance (Palmer et al., 2015). While the spectrum of
topics to be covered by any forensics educational programme can
be very broad (Nance et al., 2009; Cooper et al., 2010), the focus of
this paper is on the sample data used for training and education in
digital forensics.
A fundamental issue in forensics and security is that real-world
incriminating data is generally unsuitable for educational use
(Woods et al., 2011). In order to provide realistic data for training,
typically each educational institution creates their own emulated

* Corresponding author.
E-mail addresses: mark.scanlon@ucd.ie (M. Scanlon), xiaoyu.du@ucdconnect.ie
(X. Du), david.lillis@ucd.ie (D. Lillis).

“incriminating” digital data source for investigation, e.g., disk images, network traffic logs, mobile phones device data, etc.
Emulating accurate and viable digital evidence for use in the
classroom is an extremely arduous task. Currently this process
typically requires days or weeks of experts’ time (professors,
teaching assistants, training personnel) in creating viable digital
traces to be discovered during practical investigation training
(Yannikos et al., 2014). This project aims to effectively eliminate this
wasted time through the development of a methodology for the
automated “planting” of digital evidence in a range of standard
device images for educational purposes.
The expert effort required for the creation of viable training data
comprises of a significant planning phase, a precise execution
methodology, and trained personnel to create the resulting evidence. This will typically require manual construction (Moch and
Freiling, 2009), e.g., installing a fresh operating system on a physical PC or virtual machine, the installation of common programs
(browsers, messaging applications, email clients, file-sharing tools,
etc.), and then emulating the necessary user activity can

http://dx.doi.org/10.1016/j.diin.2017.01.010
1742-2876/© 2017 The Author(s). Published by Elsevier Ltd on behalf of DFRWS. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/
licenses/by-nc-nd/4.0/).

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M. Scanlon et al. / Digital Investigation 20 (2017) S29eS36

commence. This drawn-out process will likely result in just one
usable case study (Garfinkel et al., 2009). There are further delays in
the existing process from content creation to sharing the data with
a class. Once the viable computer activity has been created, the
computer's storage will need to be imaged and distributed. Using
current industry standard hard drive investigation tools, such as
Encase or Forensic Toolkit (FTK), the time taken to image a entire
hard disk is typically in the order of hours.
The paper is organised as follows: “Literature review” Section
outlines the need for standard corpora for digital forensic education, and reviews existing efforts to provide these. EviPlant is
introduced in “EviPlant” Section, where its motivations and functionality are discussed. To date, some initial testing has been performed, which is outlined in “Preliminary testing” Section. Finally, a
number of conclusions are drawn in “Concluding remarks” Section
and some avenues for continued research are discussed.
Contribution of this work
The contribution of this work can be summarised as follows:
 A model consisting of the maintenance of base hard drive images and a methodology for the creation, storage, categorisation,
clustering, and indexing of injectable evidence packages.
 A specification for what types of “evidence packages” would be
necessary for the creation of realistic emulated machines, e.g.,
user personae, web browsing histories, regular PC usage patterns, etc.
 The design and prototyping of a solution capable of efficiently
creating the necessary evidence packages, alongside a novel
evidence planting methodology.
Literature review
The importance of digital forensics education
In recent years, the increasing proliferation of technology in
society has led to an variety of new scenarios in which cybercrimes
can be committed. The quantity of crimes that incorporate a digital
element has grown also. This explosion in the number of cybercrimes to be investigated has led to pressure on the resources of law
enforcement agencies, with backlogs in conducting digital forensic
investigations now commonly running into years in many instances
(Casey et al., 2009; Lillis et al., 2016).
The combination of the growing quantity of investigations and
the rapidly-changing nature of technology has resulted in a strong
demand for skilled digital forensic investigators, both in industry
and law enforcement, with organisations often experiencing difficulty in filling these positions (Vincze, 2016; Vogel, 2016). Digital
Forensics education has a crucial role to play in the provision of
new trained investigators, as well as in the continued training and
proficiency testing of existing professionals to work in an everchanging technological landscape.
A looming challenge lies in the fact that training courses directly
related to digital forensics are a relatively recent phenomenon. As
technology continues to advance, existing qualifications will fast
become outdated. This will require substantial continuing professional development for investigators, as well as regular proficiency
testing of the type that is common for traditional forensics (Saks
and Koehler, 2005).
Standardised corpora
The case for standardised corpora is made by Garfinkel et al.
(2009), with the primary motivations being reproducibility and

education. In terms of education, the authors note that suitable
datasets do not occur naturally. There are significant educational,
privacy, and legal issues potentially associated with the analysis of
students' own systems, systems of students’ friends, or hard disks
purchased second-hand. The common alternative approach is for
educators to spend significant time creating customised data sets.
Al Fahdi et al. (2016) note that the public availability of forensic
cases is “very limited”. In their work, they make use of two publiclyavailable cases. The first is “Hunter XP”, which provided as a training
case for the EnCase digital investigation product. The other was a
simulated hacking case that was artificially generated by NIST as part
of the CFReDS project.1 They also gained access to two further cases
privately, which required non-disclosure agreements to be signed.
This emphasises the level of difficulty associated with obtaining
realistic cases for analysis and distribution for educational purposes.
As noted by Woods et al. (2011), the small quantity of available
corpora means that solutions to standard datasets are frequently
available online, potentially undermining the effectiveness of assessments and proficiency testing. In this scenario, it is desirable
that new, unseen challenges be posed to examinees.
Corpora characteristics
Woods et al. (2011) describe four ideal characteristics of “realistic” educational corpora (adapted):
1. Answer Keys e these are solutions to the problems posed to
students incorporating guidance as to what evidence could be
located in which digital artefacts.
2. Realistic Wear and Depth e the sample hard drive images
should contain realistic wear patterns, i.e., the hard disk image
being investigated should have regular usage surrounding
email, web browsing, application installations, file creation and
deletion, and downloaded content.
3. Realistic Background Data e a key skill for a digital investigator
to gain is the ability to decipher between pertinent and nonpertinent data on a machine. The injection of “incriminating”
data should not be obviously the only non-OS/non-application
data stored on the disk.
4. Sharing and Redistribution e as a general guideline, hard disk
images created for the purposes of education should be made
freely available for others to download.
Current approaches to providing viable disk images
The problem of providing realistic data for digital forensics education has resulted in a number of techniques being employed by
the educator. Moch and Freiling (2009) outline three existing approaches to the creation or acquisition of digital forensic datasets or
viable disk images:
 Perhaps the most widespread method is the manual creation of
disk images. Here, an instructor creates a disk image that contains specific evidence for students to find. This has the
advantage that the precise evidence is known to the instructor
and can be used for evaluation purposes. Additionally, there is
no requirement to wait for interesting activity to occur in a
natural setting, as the instructor is free to perform/emulate any
actions that are desired. However, creating these images is a
very time-consuming task, particularly given the requirement to
ideally provide realistic wear and depth.

1

http://www.cfreds.nist.gov.

M. Scanlon et al. / Digital Investigation 20 (2017) S29eS36

 A honeypot involves connecting a computer to a network with
the express intention of it being attacked and compromised
(Provos and Holz, 2007). By recording the activities of attackers,
interesting disk images can be created. However, the majority of
attacks are automated, and the quantity of images that feature
manual attacks for students to study is low. Due to the low
quantity of interesting examples available, analysis results can
frequently be found online. From a suitability standpoint, the
required analysis of these honeypot generated challenges is
often at too high a difficulty level for many learners (Woods
et al., 2011).
 A fruitful source of realistic data is second-hand hard disks. This
approach results in valuable data on naturally occurring phenomena on disks, as the disks have typically been in use by a real
user over a longer period of time than an instructor can dedicate
to the manual creation of an image (Freiling et al., 2008). Preused hard disks are the source of the Real Data Corpus, assembled over a number of years by Garfinkel (2007, 2012). This
forms part of a 30TB collection of research corpora, which also
includes items such as network packet traces, known malware
and a million document corpus gathered from the *.gov TLD.
One drawback of this approach is that it does not include materials relating to real crimes that could be used for training purposes (Baggili and Breitinger, 2015). Additionally, the use of data
belonging to real users raises a number of legal data protection
issues. As the data is generated by real users, privacy law (which
greatly varies by jurisdiction) must be taken into account, particularly when redistributing images. Images may also contain copyrighted materials (including the operating system and software) or
illegal files.
Some attempts have been made to create tools that automate
the generation of disk images for education purposes. This
approach attempts to leverage the advantages of manual creation,
while expediting the process.
Forensig2 is one such automation tool (Moch and Freiling, 2009,
2011). This allows instructors to write script files that are executed
by the system to simulate certain user behaviour. The output of the
process is an image file for students to analyse, and a “ground truth”
that can later be used to evaluate their performance. The scripting
language used by Forensig2 allows the instructor to add randomness to the build process, which facilitates the generation of distinct
images that nonetheless contain the same investigative challenge.
In order to maintain the reproducibility of challenges, a two-pass
process is used, whereby the second pass is based on an intermediary, deterministic input script. Any random decisions incorporated in the original script are taken during the first pass.
Another automated image creation tool is ForGe (Visti et al.,
2015). This also allows a user to set up a scenario, including
random elements, which results in the generation of an NTFS disk
image. In generating the image, it utilises a number of data hiding
techniques, e.g. placing data in file slack or unallocated space. Unlike Forensig,2 which is script-driven, ForGe provides a graphical
interface to facilitate the creation of images.
EviPlant
This paper introduces EviPlant as a more efficient alternative for
the creation, manipulation, storage, and distribution of digital
forensic challenges to classes of students. The fundamental premise
of EviPlant is that a base disk image file can be downloaded by
students once, and challenges can then be distributed as much

2

https://moodle.org/.

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smaller “evidence packages”. These evidence packages can then be
integrated with the base image to create the disk image for analysis.
This technique addresses a significant drawback in using separate
full image files for each challenge: namely that these images may
be in the order of tens or hundreds of gigabytes in size. For students
to download such large images during each class can be very timeconsuming, and can burden even high bandwidth networks and
servers. In an online/distance learning scenario, this problem is
further compounded by students’ own internet connection speeds,
which can vary widely, and can make distribution infeasible or
effectively impossible. This also avoids the requirement on students
to dedicate large amounts of disk space to storing multiple disk
images that they have been asked to analyse.
EviPlant consists of two core components: a diffing tool and an
injection tool. To create the challenge, an instructor can boot the
standard base image in a virtual machine, and emulate the criminal
behaviour that the students are tasked with detecting. The diffing
tool then compares the base image with the instructor's modified
image, resulting in an evidence package that includes all files and
other digital artefacts that have been created or altered, along with
their associated metadata. This package can then be distributed to
students, who then use the injection tool to “plant” this evidence
on their base images, as can be seen in Fig. 1.
Using this tool, it is possible to develop a methodology and
technical standard for the automated injection of digital evidence in a range of device images for educational purposes. It is
hoped that this will help to overcome some of the issues
encountered by Garfinkel (2012) in managing a large number of
complete disk images.
Design considerations for EviPlant
In order for EviPlant to provide the same functionality available
using current techniques and to also provide additional features, a
number of design considerations emerge including:
 Ease of Creation e Switching from the manual approach to
creating viable datasets for classes should not require significant
effort on the part of the educator. The process of creating and
curating the challenges should be straightforward.
 Efficient Distribution e One of the problems with each of the
aforementioned approaches is that the distribution of the
resultant disk images takes significant time. Courses are limited
in the range of images distributed to a class due to the
resourcing and time required for their distribution. This issue is
compounded in an online/distance learning situation. The EviPlant solution should greatly reduce the file sizes need for distribution to classes. This will enable a broader range of
investigation types to be covered by a course and provide students with viable, formative practice problems.
 Efficient Injection e The planting of the digital artefacts and
associated metadata should be as efficient as possible. As one of
the main advantages of EviPlant is facilitating a greater variety
of challenges by the learner, the impact of evidence injection
should be as minimal as possible.
 Operating System Compatibility e The operating system used by
the educator or the students should not be of concern for the
usage of the system. Likewise, the educator should be permitted
to choose the “suspect” machine's operating system according
to educational needs.
 Mobile Compatibility e Mobile device forensic training should be
possible using the system in a similar manner to the desktop/
server paradigm. The system should be compatible with modern mobile operating systems, e.g., iOS, Android, Windows
Mobile, etc.

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M. Scanlon et al. / Digital Investigation 20 (2017) S29eS36

Fig. 1. Overview of methodology.

 “Story Mode” e To eliminate the burden on the educator, the
combination of numerous evidence packages (each containing
different usage patterns) into the one challenge should be
possible. Assuming a sufficient number of evidence packages
have been created, “stories” should be constructable/manipulable from a variety of sources to create realistic problem sets.
 Answer Sets e As with any educational system, the images
generated from EviPlant should be usable from both a formative
and summative perspective. The automated generation of suitable answer sets will contribute to the reduction of effort on
behalf of the educator in the creating and grading of assignments. Due to the curated nature of the base image and specific
evidence packages (both wear-and-tear and pertinent), the
generation of answer sets for the challenges produced by the
system should be straightforward.

Evidence packages
The fundamental building blocks for EviPlant are evidence
packages. Evidence packages contain all the digital artefacts (files,
file fragments, slackspace, etc.) and associated metadata created
during the emulation of the crime. The artefacts contained within
evidence packages fall into two categories, which combine to
capture all the necessary data modified from the base image after
performing a specific task or set of tasks:
1. “Black Box” Artefacts e These are simple artefacts encapsulating
a modification from the base image. In order to use these evidence packages in a classroom deployment scenario, nothing
need necessarily be understood of their construction in order to
inject them into the base images. The answer set for these

challenges is created by the educator (effectively the script they
followed while performing the actions to be discovered).
2. “Reversed” Artefacts e The artefacts and metadata contained
within these package are understood in their entirety e effectively reverse engineered. As a result, their manipulation is
possible, e.g., SQLite databases for Internet browsing history,
VoIP application call logs, etc. In the scenario of using multiple
evidence packages for a single challenge, packages with overlapping artefacts must be manipulable to ensure that the traces
from each packages are integrated into the final disk image.
In a realistic usage scenario, a catalogue of evidence packages
would be created containing different user profiles/personae,
different application usage patterns, different browsing history,
different background noise, etc. These different evidence sources
are nestable, with an conflict (conflicting artefacts or metadata)
between later evidence packages and subsequent evidence packages being resolved by the former overwriting the latter. Seeing as
many of these wear-and-tear and background data tasks are
already being performed by educators who create their own challenges, the sharing of these background packages between educators would be of mutual benefit. Due to their minimal file sizes, the
sharing of these packages would require minimal data transfer.
Evidence creation methodology
The current approach requires the maintenance of large collections of complete disk images in order to provide a variety of
example challenges to a class. Using EviPlant, the approach changes
to creating and curating a variety of evidence packages. The creation of these evidence packages relies on the comparison or “diffing” of two disk images (“diffing” in this context stemming from the

M. Scanlon et al. / Digital Investigation 20 (2017) S29eS36

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Fig. 2. Logical evidence planting.

diff tool in *nix systems (Hunt and McIlroy, 1976)). The diffing tool
is provided with the base image used to emulate the specific user
activity and the subsequent modified image containing all traces of
the “crime”, wear-and-tear, or persona emulated. The tool scans
through the modified image and extracts all modified and newly
created artefacts (and their associated metadata) into an evidence
package, i.e., eliminating all artefacts from the modified image also
present on the base image.
In this manner, evidence packages are capable of being created
to capture small events, e.g., a boot cycle of the operating system, or
large events, e.g., a complex usage pattern to build a complete
emulated user persona over an extended period of time.
Distribution
In terms of distribution of the challenges to students, due to
the utilisation of evidence packages greatly reducing the file size
required for distribution to each member of a class, regular file
transfer services become more viable. For example, the uploading
of the requisite evidence packages to virtual learning environments such as Moodle2 or Blackboard3 becomes more feasible.
Previously, the uploading of complete disk images may not have
been possible due to file size and/or bandwidth constraints. If the
need arose to distribute larger, more complex challenges (multiple gigabytes) to students, peer-to-peer file distribution methods,
such as BitTorrent Sync (Farina et al., 2014), might be the most
performant by sharing the distribution workload among the class
themselves.
Evidence planting
The model for evidence injection centres around the initial
distribution of a base image (or collection of base images if
necessary) to each student in a class. The premise of EviPlant is that
the same base image will be used multiple times throughout the
course, thus reducing the overall volume of information to be
shared. Each base image consists of a bare installation of any
desired operating system and would be cloned before evidence
planting would commence, in order to facilitate easy reuse. In an
online/distance learning scenario, remote learners could either
download the base image first (with this base image being useful

3

http://www.blackboard.com/.

for many exercises) or they could bring their own OS (but would
have to ensure it matches that of the educator).
This model has the additional advantage that it avoids questions
of copyright infringement that may arise when distributing entire
disk images that contain an operating system and other software.
Educational institutions typically maintain their own site licenses
for software, which means that educators and students alike may
begin from a fully-licensed base image. Evidence packages can then
be shared between institutions more freely.
For the injection of “black box” packages, the evidence packages
(containing all artefacts and associated metadata) are downloaded
from a centralised server. These artefacts are placed in the base
image, overwriting any overlapping data between the base image
and the evidence package. For the “reversed” packages, manipulation of the metadata is possible before injection. For example, the
internet browsing history or VoIP call logs can be updated to have
occurred at different time than the creation of the package.
There are two options available for the evidence injection process itself:
 Logical Data Injection e This option requires the booting of the
disk image in a virtualised environment and the execution of the
injection tool natively in the guest operating system, as can be
seen in Fig. 2. The injection tool downloads the evidence package(s) from a centralised server and processes the artefacts oneby-one. Any existing files in the operating system are overwritten by those in the evidence package.
This method provides the benefit of the elimination of any
configuration issues with the injection tool running on students’
machines. In fact, if this is the injection methodology of choice, the
injection tool would come pre-installed on the base images before
initial distribution and the student can select the relevant assignment during boot. Of course, this logical injection method will leave
traces of the tool itself on the disk image, but this is unlikely to be
any concern in a educational context.
 Physical Data Injection e This option involves the modification
of the underlying blocks of the base disk image, as can be seen in
Fig. 3. The injection tool runs on the students' native operating
system and works directly with the base image file. Similarly to
the previous scenario, the tool would still download the evidence package(s) from the centralised server. The data would
be written to the disk image at their corresponding offsets,

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M. Scanlon et al. / Digital Investigation 20 (2017) S29eS36

Fig. 3. Physical evidence planting.

ignoring the make-up of the existing file system. Physical injection is the less intrusive method to the target disk image and
is necessary for verifiable disk image reconstruction. No trace of
the injection tool would be present on the resultant image.
Additional educational benefits enabled through EviPlant
The potential impact of the proposed system extends beyond
the time saved by expert educators in the creation and distribution
of realistic content for their classes and enables a number of
additional educational benefits to enhance learning:
 Helps to Eliminate Plagiarism e Custom generated, practical
digital forensic challenge exercises eliminate the possibility of
students engaging in plagiarism of results for known, freely
available corpora. After building a sufficiently large corpus of
evidence packages, it is possible that a unique disk image could
be automatically created per student with each offering their
own challenges for the students while achieving the learning
outcomes of the current topic.
 Automated Practice Exercises e The ability to create exercises
for students on-the-fly will allow students to practise their skills
on many different exercises as opposed to being limited to the
limited number of disk images made available to them.
 Timeline Emulation e Due to the minimal footprint of each
evidence package, frequent diffing becomes feasible for a
running disk image. As a result, studying the evolution of a case
study becomes possible with a different evidence package being
created at each event milestone.
 Assessment e The creation of a different challenge for each
student in a class can enable laboratory based practical assessment. Multiple students could take this test simultaneously in
the same room as a bespoke challenge could be set for each.
Outside of a educational usage scenario, EviPlant could also be
used for the curation of evidence packages and challenges suitable for:

 Proficiency Testing e In order to provide viable proficiency
testing for digital forensic investigators, a comprehensive suite
of challenges must be available. Building a sufficient catalogue of
evidence packages allows the quick creation of challenges for
proficiency testing containing numerous permutations and
combinations of the catalogue. In the scenario of multiple investigators being tested simultaneously at the same location,
each investigator could be tested for the same skills while
working on different challenges.
 Forensic Software Testing and Validation e The testing and
validation of forensic software is an important issue (Beebe,
2009; Guo et al., 2009). Often, forensic software is tested
against common datasets, which are also generally available to
the software developers. Another potential use for EviPlant is to
create corpora on which forensic software can be tested, and
evaluated with regard to its success rate in identifying the
simulated criminal behaviour.
 Point-in-Time Reconstruction e Using a similar technique as
that outlined above for timeline reconstruction training, high
frequency package creation would enable the real-time
monitoring and reconstruction of the device state at any
point necessary. In order to increase the frequency of the
packages, they would be created against the previous snapshot as opposed to the original base image. This would then
enable point-in-time reconstruction by sequentially integrating the evidence packages in order of their acquisition
time e similar to the incremental backup approach used by
database administrators.
 Malware Analysis e By intentionally installing malware on a test
image, the diffing tool can be repurposed to provide evidence
packages that documents the malware's lifecycle on a target
system. After the fact, the system can be reconstructed for the
analysis of the malware at any stage in its lifecycle.
 International Collaboration e Current international collaboration techniques typically involve the shipping of seized devices
to the requesting agency. Using the proposed approach in

M. Scanlon et al. / Digital Investigation 20 (2017) S29eS36

combination with a data deduplication system, such as that
proposed by Scanlon (2016), can greatly expedite the international transmission of evidence.
Preliminary testing
To demonstrate the viability of the proposed system, the two
main components of the system (namely the diffing tool and the
injection tool) were developed in Python using the pytsk4 library
for disk image analysis. pytsk is a python wrapper for The Sleuth
Kit.5 The Sleuth Kit provides a wealth of file system compatibility
including NTFS, FAT, ExFAT, UFS 1, UFS 2, EXT2, EXT3, EXT4, HFS, ISO
9660, and YAFFS2. For testing purposes, a Windows 10 virtual
machine was created and used as the base image. For each test, the
base image was cloned, booted and user activity was emulated on
the machine.
In terms of the testing of the diffing tool, a variety of usage
patterns were tested ranging from a single boot cycle of the
virtual machine, to an extended session involving internet
browsing, application installation, file downloading, multiple
boot cycles, etc. In each scenario, the diffing tool discovered all of
the modified artefacts relating to recorded usage, including a
number of operating system files that were modified during the
regular usage of the virtual machine (e.g., $MFT, pagefile.sys,
etc.). These artefacts and associated metadata were output into
an evidence package.
To assess the injection methodology, the base operating system
was booted and the injection tool was run locally on the live machine to perform a logical injection of the artefacts, as described in
“Evidence planting” Section. The injection tool took each individual
artefact and added it to the virtual disk. In the eventuality of conflicting artefacts, the version from the base image was overwritten
by that from the evidence package. To demonstrate the viability of
the resultant generated disk images and to confirm the injection of
the necessary artefacts, these images were subsequently analysed
using EnCase and, unsurprisingly, the pertinent planted evidence
was identifiable and recoverable.
Concluding remarks
The solution presented as part of this paper makes the creation of digital forensic challenges easier for the educator. More
digital forensic challenges are capable of being stored in the same
disk capacity than if entire disk images were used. This reduction
in required storage can facilitate more challenges being given to
students to enhance their learning, i.e., the distribution time and
local storage required on students’ machines are both reduced.
While EviPlant is focused on enhancing digital forensics education, the approach outlined above for the creation and manipulation of hard disk images can also be applied to complimentary
fields, e.g., digital forensic tool testing and validation, virtual
machine/cloud instance monitoring, point-in-time reconstruction, etc.
Future work
The EviPlant system outlined as part of this paper is currently a
functional prototype. As with any system at the prototype stage,
there are a number of desired features that will be developed upon
in the future including:

4
5

https://github.com/py4n6/pytsk.
http://www.sleuthkit.org/.

S35

 Manipulation of Evidence Packages e If components of the evidence package are understood, e.g., SQLite database, file traces,
etc., the manipulation of these components should be possible.
 Collision Resolution e Currently, the integration of evidence
packages into a base image overwrites any artefacts that were
previously contained in the image. Multiple evidence packages
can be integrated in succession, but any collisions, i.e., the same
artefact contained in both packages, will result in that artefact
being overwritten. To provide intelligent collision resolution (for
both “black box” and “reversed” packages) would greatly
expand the complexity of challenges creatable and the broader
usefulness of the tool.
 Physical Injection e Adding forensic artefacts to the disk
without initially booting the OS is desirable as any traces of the
injection tool itself executing on the disk image would be
eliminated. While not an absolute requirement for educational
purposes, for a number of the complimentary applications of the
technology this may prove necessary.
 Comprehensive Evaluation e Implementation and evaluation of
a real-world deployment of the tool in both face-to-face and
online/distance classroom settings.

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