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An Approach to Train and Evaluate the Cybersecurity Skills of Participants in Cyber Ranges based on Cyber-Risk Models

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There is an urgent need for highly skilled cybersecurity professionals, and at the same time there is an awareness gap and lack of integrated training modules on cybersecurity related aspects on all school levels. In order to address this need and bridge the awareness gap, we propose a method to train and evaluate the cybersecurity skills of participants in cyber ranges based on cyber-risk models. Our method consists of five steps: create cyber-risk model, identify risk treatments, setup training scenario, run training scenario, and evaluate the performance of participants. The target users of our method are the White Team and Green Team who typically design and execute training scenarios in cyber ranges. The output of our method, however, is an evaluation report for the Blue Team and Red Team participants being trained in the cyber range. We have applied our method in three large scale pilots from academia, transport, and energy. Our initial results indicate that the method is easy to use and comprehensible for training scenario developers (White/Green Team), develops cyber-risk models that facilitate real-time evaluation of participants in training scenarios, and produces useful feedback to the participants (Blue/Red Team) in terms of strengths and weaknesses regarding cybersecurity skills.
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An Approach to Train and Evaluate the Cybersecurity Skills of
Participants in Cyber Ranges based on Cyber-Risk Models
Gencer Erdogan1, Åsmund Hugo1, Antonio Álvarez Romero2,
Dario Varano3, Niccolò Zazzeri4 and Anže Žitnik5
1Software and Service Innovation, SINTEF Digital, Oslo, Norway
2Research & Innovation, Atos, Seville, Spain
3Department of Information Engineering, University of Pisa, Pisa, Italy
4Trust-IT Services, Pisa, Italy
5XLAB, Ljubljana, Slovenia
{gencer.erdogan, aasmund.hugo}@sintef.no, antonio.alvarez@atos.net, dario.varano@ing.unipi.it,
n.zazzeri@trust-itservices.com, anze.zitnik@xlab.si
Keywords: Cyber Range, Cybersecurity, Cyber-risk Models, Training Scenario, Exercise, Evaluation, Real-time, White
Team, Green Team, Blue Team, Red Team.
Abstract: There is an urgent need for highly skilled cybersecurity professionals, and at the same time there is an
awareness gap and lack of integrated training modules on cybersecurity related aspects on all school levels.
In order to address this need and bridge the awareness gap, we propose a method to train and evaluate the
cybersecurity skills of participants in cyber ranges based on cyber-risk models. Our method consists of five
steps: create cyber-risk model, identify risk treatments, setup training scenario, run training scenario, and
evaluate the performance of participants. The target users of our method are the White Team and Green Team
who typically design and execute training scenarios in cyber ranges. The output of our method, however, is
an evaluation report for the Blue Team and Red Team participants being trained in the cyber range. We have
applied our method in three large scale pilots from academia, transport, and energy. Our initial results indicate
that the method is easy to use and comprehensible for training scenario developers (White/Green Team),
develops cyber-risk models that facilitate real-time evaluation of participants in training scenarios, and
produces useful feedback to the participants (Blue/Red Team) in terms of strengths and weaknesses regarding
cybersecurity skills.
1 INTRODUCTION
There is an urgent need for highly skilled, multi-
disciplined cybersecurity professionals, given the
increasingly aggressive cyber-landscape public and
private organizations are facing. As pointed out by the
European Cyber Security Organization (ECSO), at
education level, there is a big awareness gap and lack
of integrated training modules on cybersecurity
related aspects on all school levels, starting from low
awareness and skills of teachers themselves. The
same is true for professional training on university
level, including lack of cybersecurity modules in
higher education training programs for vital service
domains etc. In addition, there are only few existing
cybersecurity higher education programs in Europe.
Moreover, it is reported that at professional level,
there is a lack of accessible tools for continuous
awareness, training and skills development on
cybersecurity aspects (ECSO, 2016).
In order to address the need of continuous
cybersecurity awareness, training and skills
development, we have developed a method to train
and evaluate the cybersecurity skills of participants in
cyber ranges based on cyber-risk models. Our method
assumes that the technical capabilities to simulate
infrastructure on which exercises are executed
already exist and that necessary instructions and
training for the cyber range has already been given to
the participants.
Thus, the contribution of this paper is a method
for creating cyber-risk models that facilitate the
training and evaluation of cybersecurity skills of
participants in cyber-ranges. The method is described
using an example in the context of
CYBERWISER.eu, which is a web-based cyber
Erdogan, G., Hugo, Å., Romero, A., Varano, D., Zazzeri, N. and Žitnik, A.
An Approach to Train and Evaluate the Cybersecurity Skills of Participants in Cyber Ranges based on Cyber-Risk Models.
In Proceedings of the 15th International Conference on Software Technologies (ICSOFT 2020), pages 509-520
ISBN: 978-989-758-443-5
Copyright ©2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reser ved
509
range platform we have developed as part of the
international EU project with the same name
(CYBERWISER.eu, 2020a).
Cyber ranges often have different groups of
people referred to as "teams" who have different
roles. In this paper, we consider the Green, White,
Red, and Blue teams (Damodaran & Smith, 2015).
The Green Team consists of individuals who operate
the range infrastructure and support tool systems. In
collaboration with the White Team, the Green Team
manages on-demand definition of training scenarios.
The White Team represents the instructor/s of the
training, whether course based or as an exercise. The
White Team collaborates with the Green Team to
deploy and configure training scenarios. The White
Team also evaluates the participants' progress. The
Red Team carries out cyberattacks against the
infrastructure simulated on the cyber range as part of
a training scenario. The Blue Team detects and
responds to the attacks performed by the Red Team
and/or automatically by the tools in the cyber range.
To address the abovementioned needs and
develop artefacts that appropriately meet these needs,
we define the following success criteria.
Success Criterion 1: The method must be easy to
use and comprehensible for cyber-range training
scenario (exercise) developers.
The main target user group of our method is people
who design and develop cyber-range training
scenarios/exercises. That is, the Green Team and the
White Team. The method must therefore be easy to use
and comprehensible for the intended target audience.
Success Criterion 2: The method must provide
necessary guidelines to create cyber-risk models that
facilitate real-time evaluation of participants.
Cyber range training scenarios are dynamic in
nature and the participants being trained need to make
decisions and take actions on-the-fly. Thus, to
correctly evaluate the cybersecurity skills of the
participants, we need to evaluate their decisions and
actions in real-time while the exercise is running.
Success Criterion 3: The method must produce
useful feedback to the participants in terms of
exercise evaluations.
For the participants to learn from their decisions
and actions taken in an exercise in the cyber range,
they need to receive feedback explaining to what
extent they have successfully carried out the exercise.
Thus, we need to provide useful feedback to the
participants evaluating their achievements.
In Section 2, we describe our research method. In
Section 3, we describe the architecture of our cyber
range platform, before we explain our method for
training and evaluation in Section 4. In Section 5, we
describe related work. In Section 6, we discuss the
extent to which we have fulfilled our success criteria
described above, before concluding in Section 7.
2 RESEARCH METHOD
Figure 1 illustrates the three steps of our research
method, which is in line with the design science
approach by Wieringa (2014). Although the steps are
illustrated sequentially, the method was carried out
iteratively where some of the steps were revisited
during the process.
Figure 1: Research method.
In Step 1, we identified three success criteria
which act as requirements for our method for training
and evaluation based on the background and needs as
explained in Section 1.
In Step 2, we developed our method for training
and evaluation. The method consists of five main
steps: create cyber-risk model, identify risk
treatments, setup training scenario, run training
scenario, and finally evaluate the performance of
participant. All steps are supported by tools that
collectively comprise our cyber range training
platform. The cyber range training platform will be
explained in more detail in Section 3.
In Step 3, we evaluated our method for training
and evaluation in real-world pilot studies to assess the
feasibility of our approach w.r.t. our success criteria.
3 CYBER RANGE
ARCHITECTURE
Before we describe the steps of our method for
training and evaluation, it is necessary to explain the
overall architecture of our cyber range platform in
which the training and evaluation method is applied.
As illustrated in Figure 2, the platform consists of
components that may be grouped into four parts:
simulated infrastructure, scenario environment, cyber
range, and user interface. The Simulated
Infrastructure represents the first layer of the
platform. This layer consists of virtual machines and
virtual networks that simulate an organization's ICT
system. Depending on the objective of a training
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scenario, the participant being trained will either
carry out attacks on the simulated ICT system
automatically using the Attack Simulator, or mitigate
an ongoing attack using the Countermeasure
Simulator. Manual attacks/mitigations are also
possible. The participants in a Blue or Red Team may
also run scans to check for potential vulnerabilities
using the Vulnerability Assessment Tools.
The Monitoring Sensors are software programs
implemented in the simulated ICT system to monitor
host activity or network activity. The host activity
sensors detect potential threats, while the network
activity sensors analyse network tr affic a nd dete ct and
prevent network intrusion. These sensors send events
to the Anomaly Detection Reasoner and the
Economic Risk Evaluator.
The second layer of the cyber range platform is
the Scenario Environment which is implemented as
an IaaS and contains the components necessary for
the training scenarios. At the centre of the Scenario
Environment, we find the Economic Risk Evaluator,
which uses Economic Risk Models to produce real-
time risk assessment in terms of monetary loss based
on the observed behaviour of the participants in the
training scenario. The real-time feature builds on
continuous observation of the dynamic behaviour of
the training scenarios with the help of the Monitoring
Sensors, Anomaly Detection Reasoner, and the
Vulnerability Assessment Tools which all produce
input to the Economic Risk Evaluator.
To create a realistic experience for the
participants, the Attack Simulator can be configured
to automatically launch pre-defined attacks towards a
specific target. The Performance Evaluator is notified
about the success of an automated attack which
indicates whether the participants, acting as
defenders, were able to prevent the attack. Similarly,
the Countermeasure Simulator can execute mitigation
measures that prevent further attacks automatically
after a certain event has occurred.
The Performance Evaluator component takes as
input the risk assessment produced by the Economic
Risk Evaluator, as well as actions taken by the
participants, and based on these inputs produces an
evaluation report. This evaluation report is forwarded
to the Centralized Logging Component where it is
archived and made available to the participant.
The third layer of the cyber range platform is the
Cyber Range, which includes the components Digital
Library, Training Manager, and Simulated
Infrastructure Manager, as well as the Scenario
Environment and the Simulated Infrastructure. The
Training Manager provides user interfaces for easy
design and configuration of training scenarios, their
creation, deployment, as well as un-deployment and
resource removal after a completed training session.
The Digital Library is a repository storing all the
virtual machine images and additional software,
required for building the training scenarios, as well as
the designed scenarios themselves. The Simulated
Infrastructure Manager acts as an interface to the
underlying IaaS to control the virtual machines and
networks of the Scenario Environments based on
instructions from the Training Manager.
Additionally, Simulated Infrastructure Manager
provides the participants access to the virtual
Figure 2: Cyber range architecture.
An Approach to Train and Evaluate the Cybersecurity Skills of Participants in Cyber Ranges based on Cyber-Risk Models
511
machines in the Scenario Environment by exposing a
VNC interface, simply available through the user’s
web browser.
The fourth and final layer of the cyber range
platform is the User Interface consisting of a Web
Portal and the Cross-learning Facilities. The
CYBERWISER.eu Web Portal is the single entry-
point to the platform and its services for all end-users
(white, green, blue, red teams, etc.). The
authentication process is provided by the Cross-
Learning Facilities component through a Single Sign-
On (SSO) service. The Cross-Learning Facilities
component provides, among others, training
materials in terms of literature, courses,
communication tools such a chat service, dashboards
for the users and a link to the Cyber-Range Service.
Scores, achieved by participants in the Cyber Range
training exercises, are transferred to the Cross-
Learning Facilities and can be viewed here as well.
4 METHOD FOR TRAINING AND
EVALUATION
Figure 3 illustrates our method for training and
evaluating cybersecurity skills of participants in
cyber ranges based on cyber-risk models. The
following sections explain each of the steps.
Figure 3: Method for training and evaluation.
4.1 Step 1: Create Cyber-Risk Model
As indicated in Figure 3, the first step of our method
expects as input a description of a training scenario,
which is basically a description of an exercise to be
carried out on the cyber range platform with the
purpose of training cybersecurity skills. This
description is expected to be provided by
cybersecurity experts. For example, someone who
has the role as Chief Security Information Officer and
who is interested in training their cybersecurity staff.
Examples of roles to be trained are Vulnerability
Analysts or Threat Analysts (CIISec, 2019). As part
of our cyber range platform, we do also provide
guidelines for how to describe training scenarios.
However, these guidelines are out of the scope of this
paper. The reader is referred to (CYBERWISER.eu,
2019b) for a detailed explanation on how to describe
training scenarios.
In the following, we consider a training scenario
example developed as part of applying the method in a
real-world case pilot as part of the CYBERWISER.eu
project. The training scenario we consider describes an
exercise to train technical security staff in mitigating an
SQL injection attack. That is, the exercise concerns
defensive training where the technical security staff
needs to mitigate an SQL injection attack their ICT
infrastructure, which is simulated on the cyber range
platform, is exposed to. From a cyber range training
perspective, the team trained in defensive exercises are
typically referred to as the Blue Team.
Based on the above training scenario description
we follow an approach described in earlier work
(Erdogan, Gonzalez, Refsdal, & Seehusen, 2017) to
first create a graphical cyber-risk model using
CORAS (Lund, Solhaug, & Stølen, 2011) in order to
capture the SQL injection attack pattern, and then
schematically develop a corresponding machine-
readable risk assessment algorithm with respect to the
graphical risk model. The CORAS risk model and the
risk assessment algorithm are the output of Step 1.
Figure 4 illustrates the CORAS risk model created
for the SQL injection training scenario example
described above. When creating risk models, we
make use of existing libraries and catalogues such as
CAPEC (CAPEC, 2020), OWASP (OWASP, 2020)
and CWE (CWE, 2020) in order to create risk models
that are in line with standard attack patterns.
The CORAS risk model in Figure 4 illustrates that
a threat Hacker initiates the treat scenario S1: Initiate
SQL Injection. Moreover, the hacker may exploit the
vulnerabilities CWE-89: Improper neutralization of
special elements used in an SQL command and CWE-
390: Detection of SQL-related error conditions
without action which leads to the unwanted incident
U1: SQL injection successful. Finally, we see that the
unwanted incident has an impact on the security
assets A1: Confidentiality and A2: Integrity, which
are the assets we want to protect.
In addition to threats, threat scenarios, vulnerabi-
lities, unwanted incidents, and security assets, we use
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CORAS risk models to capture the risk assessment
values likelihood, conditional likelihood and
consequence. In a standard CORAS risk model, these
values are written directly in the model. However, in
our approach we parameterize these values in the
model to later develop the corresponding risk
assessment algorithms. Considering our example in
Figure 4, the likelihood of S1 is represented by l_S1,
the conditional likelihood going from S1 to U1 is
represented by cl_S1_to_U1, the likelihood of U1 is
represented by l_U1, the consequence of U1 on A1 is
represented by c_U1_A1, and the consequence of U1
on A2 is represented by c_U1_A2.
Finally, to capture the dynamic behaviour of the
SQL injection attack that the simulated infrastructure
is exposed to during the training scenario, we include
what we refer to as indicators in the risk models. By
indicator we mean a piece of information that is
relevant for assessing the risk level. The risk level in
our approach is represented as monetary loss. We
distinguish between the following four kinds of
indicators.
Business configuration (IN-32, IN-C81C, and
IN-C81I): Indicator values are obtained by
asking business related questions. The
indicator values are thus based on the
knowledge of the participant.
Test results (IN-37): Indicator values are
obtained by carrying out tests, such as
vulnerability scans or automated attacks. The
indicator values are thus based on test results.
Test results and business configuration
indicators are non-intrusive in the sense that
they do not require the implementation of
sensors in the simulated infrastructure.
Network-layer monitoring: Indicator values are
obtained by monitoring the network layer. This
Figure 4: CORAS risk model with indicators.
type of indicator is intrusive in the sense that
sensors need to be deployed in the network
layer of the simulated infrastructure under
analysis.
Application-layer monitoring (IN-44 and IN-
56): Indicator values are obtained by
monitoring the application layer. This type of
indicator is also intrusive; a sensor needs to be
installed in the machine under analysis.
Note that Figure 4 does not illustrate any network
monitoring indicator as they were not relevant for this
example.
Having created the graphical risk model, next we
schematically translate the model into an executable
risk assessment algorithm in terms of an R script (R-
project, 2020). Figure 5 illustrates an excerpt of the R
script created based on the risk model in Figure 4. The
excerpt illustrates that we represent the risk model as
a Bayesian Network in order to do calculations using
the likelihood and consequence values captured in the
risk model. The likelihood and consequence values
are in turn calculated with respect to their respective
indicators. Due to space restrictions, it is
Figure 5: Excerpt of R script.
An Approach to Train and Evaluate the Cybersecurity Skills of Participants in Cyber Ranges based on Cyber-Risk Models
513
not possible to show the complete R script. However,
the logic for computing likelihood and consequence
values are as follows:
The indicators used to assess likelihood values
(IN-32, IN-37, IN-44, and IN-56) are
formulated either as Yes/No questions or as
quantitative questions. Based on the answer,
we increase or decrease the likelihood value of
the risk illustrated in Figure 4. For example,
consider that the answer to indicators IN-32
and IN-44 are "Yes", and that the answer to
indicator IN-56 is "25 in the last 10 minutes",
then we would increase the likelihood l_S1 to
Very High (in a scale of {Very Low, Low,
Medium, High, Very High}).
The indicators used to assess consequence
values (IN-C81C and IN-C81I) basically asks
the participant what the consequence of an
unwanted incident is, given that the unwanted
incident materializes.
For the complete R script, the reader is referred to
a technical report in which the risk model in Figure 4
as well as other risk models tried out in context of
real-world pilots are explained in detail
(CYBERWISER.eu, 2020b).
4.2 Step 2: Identify Risk Treatments
In Step 2, we base ourselves on the risk model created
in Step 1 to identify risk countermeasures. We
identify countermeasures using CORAS treatment
diagrams which represent strategies and action plans
the implementation of which reduces risks to an
acceptable level (Lund et al., 2011).
Figure 6 illustrates the same risk model as in
Figure 4. However, in Figure 6, we have removed all
indicators and identified several risk countermeasures
for the vulnerabilities CWE-89 and CWE-390. In
total, we identified 10 countermeasures for
vulnerability CWE-89, and 2 countermeasures for
vulnerability CWE-390, but due to space restrictions
we illustrate 5 of 12 countermeasures in the treatment
diagram in Figure 6. The main source from which we
identified the countermeasures were the webpages
documenting CWE-89 and CWE-390 (CWE, 2020).
Having created CORAS treatment diagrams, next
we describe each countermeasure in detail using a table
template including a unique ID of the countermeasure
(for example, M8 illustrated in Figure 6), the name of
the countermeasure (for example, Validate input), a
detailed description of the countermeasure, and source
of the countermeasures (for example, URL to specific
countermeasures in CWE). An excerpt of the detailed
description of countermeasure M8, according to CWE-
89 (CWE, 2020), is: "When performing input
validation, consider all potentially relevant properties,
including length, type of input, the full range of
acceptable values, missing or extra inputs, syntax,
consistency across related fields, and conformance to
business rules. As an example of business rule logic,
'boat' may be syntactically valid because it only
contains alphanumeric characters, but it is not valid if
the input is only expected to contain colours such as
'red' or 'blue'."
Thus, the output of Step 2 is a CORAS treatment
diagram with countermeasures and an accompanying
table, according to the template described above,
describing the countermeasures in detail. These out-
puts are used in the cyber training scenarios to provide
the participants a set of countermeasure options.
4.3 Step 3: Setup Training Scenario
In Step 3, we setup the training scenario in the cyber
range platform. All the components in the cyber range
platform (described in Section 3) play a role in setting
Figure 6: CORAS treatment diagram.
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up and executing a training scenario. However, our
method considers mainly the components that are
relevant for training and evaluating based on risk
models. When describing the setup and execution of
training scenarios, we therefore mainly discuss the
components Economic Risk Evaluator, Counter-
measure Simulator, and Performance Evaluator, and
assume that all other components are setup and work
properly.
As illustrated in Figure 7, setting up the training
scenario depends on the risk models. As illustrated in
the top-left part of Figure 7, we first develop risk
models with indicators and then schematically
translate a risk model with indicators to an R script
(that is, Step 1 explained in Section 4.1). Next, as
illustrated on bottom-left part of Figure 7, we develop
treatment diagrams (risk models with
countermeasures) and then describe all identified
countermeasures in detail using a table-based
template (that is, Step 2 explained in Section 4.2).
The R scripts are implemented in the Economic
Risk Evaluator, which has the responsibility of
executing the scripts. This includes retrieving values
from the components Monitoring Sensors, Anomaly
Detection Reasoner, and Vulnerability Assessment
Tools and assign the values to their corresponding
indicators in the risk model. Recall that we have
different types of indicators as described in Section
4.1. The component Vulnerability Assessment Tools
provide values to test result indicators, the component
Monitoring Sensors and Anomaly Detection
Reasoner collectively provide values to network layer
monitoring indicators and application layer
monitoring indicators.
Each countermeasure identified and described as
part of Step 2 is implemented in the Countermeasure
Simulator. The Countermeasure Simulator is a
component that has a register of a set of possible
countermeasures for each risk model considered in a
training scenario. Basically, the Countermeasure
Simulator is responsible of making a set of relevant
countermeasures available to the participants during
the training scenario.
4.4 Step 4: Run Training Scenario
In Step 4, we execute the training scenario with
respect to the training scenario configuration carried
out in Step 3.
The execution of a training scenario depends on
the use case of the training scenario. There are four
kinds of use cases: (A) Blue Team vs. Red Team
where the purpose is for the Blue Team to protect the
simulated ICT system from cyber-risk attacks
performed by the Red Team, (B) Red Team vs. Blue
Team where the purpose is for the Red Team to attack
the simulated ICT system protected by the Blue
Team, (C) Blue Team vs. Cyber Range Platform
where the purpose is for the Blue Team to protect the
simulated ICT system from cyber-risk attacks
automatically carried out by the platform, and finally
(D) Red Team vs. Cyber Range Platform where the
purpose is for the Red Team to attack the simulated
ICT system protected by the platform.
Figure 7: Setting up training scenario in the cyber range.
An Approach to Train and Evaluate the Cybersecurity Skills of Participants in Cyber Ranges based on Cyber-Risk Models
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Considering our example described in Section
4.1, the purpose is for the Blue Team to protect the
simulated ICT system from SQL injections. In this
example, we do not consider a team versus another
team. Thus, we are considering use case (C) Blue
Team vs. Cyber Range Platform. The automatic SQL
injections are initiated by the person having the role
as tutor/teacher using the component Attack
Simulator in the platform. The person having the
teacher/tutor role is typically referred to as a member
of the White Team in context of cyber ranges.
Assuming members of the Blue Team and White
Team are logged in the cyber range platform, the
training scenario will commence as follows. First, the
White Team initiates SQL injections using the
component Attack Simulator. The Blue Team
monitors the status of the simulated ICT system via
the graphical user interface of the Economic Risk
Evaluator. Then, if the Blue Team detects that the risk
level for SQL injection is going up, they need to
investigate how and where the attack is carried out
(on the simulated ICT system) and select appropriate
countermeasures from the Countermeasure Simulator
and apply them. Having applied the countermeasures,
the Blue Team continuously checks the risk level to
see if the level goes down or up. The important task
here is to understand what kind of cyber-risk attack
the system is exposed to and based on that select
appropriate countermeasures. After a predefined time
(running time of the exercise), the White Team stops
the exercise. All actions taken by the Blue Team is
logged. After the exercise has been stopped, the risk
assessment and the countermeasures selected by the
Blue Team are fed into the Performance Evaluator as
illustrated in Figure 7.
4.5 Step 5: Evaluate the Performance
of Participants
Based on the training scenario results obtained by
carrying out the exercise in Step 4, the Performance
Evaluator calculates automatically in Step 5 how well
the participants have carried out the training scenario.
The Performance Evaluator considers the current
status of the exercise, but also its history, that is, how
the exercise has evolved.
The evaluation is carried out with respect to
predefined assessment algorithms implemented in the
Performance Evaluator, which vary depending on the
exercise. Each performance evaluation algorithm
defined and implemented in the Performance Evalua-
tor expects a series of inputs that must be obtained from
the training scenario exercise. The values of those
inputs are assigned by analysing and processing the
raw logs the exercise generates (output of Step 4).
In the following the different types of information
leveraged to produce the performance evaluation
reporting for the participants are listed, along with
some examples:
Flags. Flags mark relevant moments in the
exercise, mainly related to achievements of the
participant. For example, the execution of a
Denial of Service attack by a participant who is
part of the Red Team or the blocking of the
TCP for a suspicious IP address by a participant
who is part of the Blue Team. The Attack
Simulator and the Countermeasure Simulator
are the main components producing this
information (flags).
Elaborated logs coming from monitoring tools
deployed in the monitored infrastructure. Such
logs produce information about, for example,
an attacker performing a dictionary attack to
achieve brute-force login into a certain
machine.
Results of vulnerability scans executed against
a certain infrastructure element. The
vulnerability scans (part of the Vulnerability
Assessment Tools) are useful for the Red Team
to find weaknesses that can be exploited. It can
also be used by a Blue Team to identify
vulnerabilities that need to be mitigated. An
example of a vulnerability may be the improper
neutralization of special characters in an SQL
query which may eventually be exploited with
an SQL injection attack.
Cyber risk exposure evolution: the amount of
money being exposed and its evolution over
time is relevant for the evaluation. Pairs (cyber
risk, timestamp) are fed to the Performance
Evaluator from the Economic Risk Evaluator to
describe the corresponding trajectory. Using
configured thresholds, the Performance
Evaluator will analyse this trajectory to
evaluate the celerity and effectiveness the
participant showed to react to the attack
(selected countermeasures by the participant).
Questionnaire: In addition to the above
information, the Performance Evaluator takes
input coming from a questionnaire filled out by
the White Team evaluating the participants.
The predefined evaluation algorithm in the
Performance Evaluator is configured to give
different scorings depending on the answer
given by the White Team.
In the case of our SQL injection exercise example,
the participant playing the role of defender is
presented with different alternatives in terms of
countermeasures, thanks to the Countermeasure
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Simulator. The countermeasures are presented to the
participant in terms of preconfigured scripts.
However, some of the countermeasures are more
useful than others (expressed as ratings in each
countermeasure), and each countermeasure comes
with a cost expressed in monetary value.
To make the exercise represent a real-world
situation, a limited budget is allocated for the
participant to select ("buy") countermeasures and
apply them on the underlying cyber risk attack.
When choosing a countermeasure, the
corresponding script is executed, and this event is
registered with a timestamp and sent to the
Performance Evaluator. The Economic Risk Evaluator
sends the evolution of the cyber risk exposure which is
also used as input of the performance evaluation
algorithm. Recall that, depending on the selected
countermeasure, the risk level may vary.
Based on the information produced by the
participant during a training scenario as pointed out
above, the Performance Evaluator will be able to
evaluate whether:
The Blue Team correctly identified the cyber-
attack that the simulated ICT system is exposed
to.
The Blue Team selected correct and
appropriate countermeasures.
The selected countermeasures were the most
cost-efficient.
The Blue Team mitigated the cyber-risk attack
within the expected time of the training
scenario.
In addition to the above, the White Team has the
possibility to provide their own assessment based on
their expert knowledge by filling out the
abovementioned questionnaire. The questionnaire
provides the assessment carried out by the White
Team in a structured manner to the Performance
Evaluator.
As indicated in Figure 3, the main output of the
Performance Evaluator is a report which provides a
grading and evaluates the performance of the
participants. To provide a richer feedback, this grade
is broken into chapters which provide the participants
a more detailed assessment explaining aspects the
participants showed more strength and where the
weak points are.
5 RELATED WORK
Cyber ranges have traditionally been developed and
used by military institutions for cybersecurity training
in the context homeland defence strategy
(Damodaran & Smith, 2015; Davis & Magrath, 2013;
Ferguson, Tall, & Olsen, 2014). However, over the
years, there have been proposed various cyber range
solutions to bring cybersecurity training to both
public and private organizations (Yamin, Katt, &
Gkioulos, 2020).
Secure Eggs (Essentials and Global Guidance for
Security) by NRI Secure (NRISecure, 2020), enPiT-
Security (SecCap) (EnpitSecurity, 2020), and CYber
Defense Exercise with Recurrence (CYDER) are
approaches and security training programs focusing
on basic cybersecurity hands on and awareness
training (Beuran, Chinen, Tan, & Shinoda, 2016).
There are various approaches focusing on
cybersecurity skills training within specific domains
such as smart grid (Ashok, Krishnaswamy, &
Govindarasu, 2016) and cybersecurity assurance
(Somarakis, Smyrlis, Fysarakis, & Spanoudakis,
2019). In contrast to domain specific approaches, our
approach is generic and may be applied for training
cybersecurity skills in any domain. This is also
demonstrated by the fact that we have applied our
approach in the context of three different large scale
real-world pilots: academic pilot, transport pilot, and
energy pilot (CYBERWISER.eu, 2020a).
Several approaches focus mainly on the cyber
range architecture and improving the efficiency and
performance of cyber ranges. Pham, Tang, Chinen,
and Beuran (2016) suggest a cyber range framework
named CyRIS/CyTrONE focusing on improving the
accuracy of the training setup, decreasing the setup
time and cost, and making training possible
repeatedly and for a large number of participants. The
authors also report on an evaluation of the
performance of their approach (Beuran et al., 2018).
Russo, Costa, and Armando (2018) argue that the
design, validation, and deployment of scenarios are
costly and error-prone activities that may require
specialized personnel for weeks or even months, and
that misconfiguration in the resulting scenario can
spoil the entire cyber exercise. To address these
challenges brought by architectural shortcomings, the
authors propose a framework for automating the
design, model validation, generation and testing of
cyber training scenarios. As part of developing our
cyber range platform described in Section 3, we
considered requirements related to scalability and
efficiency of deploying and running training
scenarios. However, as explained in Section 1, the
contribution of this paper is our method for creating
cyber-risk models that facilitate the training and
evaluation of cybersecurity skills of participants in
cyber-ranges. The reader is referred to a technical
report for further information on considerations
An Approach to Train and Evaluate the Cybersecurity Skills of Participants in Cyber Ranges based on Cyber-Risk Models
517
related to scalability and efficiency of our cyber range
platform (CYBERWISER.eu, 2019a).
The approaches provided by Russo et al. (2018)
and Braghin et al. (2019) are similar to our approach
in the sense that they use some form of attack models
as a foundation to design and execute training
scenarios. Russo et al. (2018) introduce a Scenario
Definition Language (SDL) based on the OASIS
Topology and Orchestration Specification for Cloud
Applications (TOSCA). Braghin et al. (2019) provide
a domain specific language for scenario construction
in which it is possible to capture configuration
problems as well as structural vulnerabilities. We use
CORAS risk modes which are acyclic directed graphs
as described in Section 4. Thus, from a modelling
perspective, our approach complements existing
approaches. To the best of our knowledge, our
approach is unique compared to existing approach in
the sense that we develop machine readable risk
assessment algorithms based on the cyber-risk
models in order to facilitate real-time risk assessment
as well as real-time evaluation of the participants'
cybersecurity skills as explained in Section 4.
In their systematic literature review, Yamin et al.
(2020) report that current cyber range approaches that
apply some form of cyber-attack modelling are not
validating the models against real world scenarios and
use mostly artificial educational scenarios. In this
respect, our approach requires that the risk models are
developed with respect to training scenario
descriptions requested by stakeholders. Based on
experience so far in applying our method in real-
world academic pilot, transport pilot, and energy
pilot, it is reasonable to argue that our approach
validates the risk models against real world scenarios,
thus supporting its feasibility.
6 EVALUATION
In this section, we discuss the extent to which we have
fulfilled our success criteria described in Section 1.
6.1 Fulfilment of Success Criterion 1
The first criterion states: The method must be easy to
use and comprehensible for cyber-range training
scenario (exercise) developers.
The steps of our method (Section 4) are well in line
with activities typically carried out in cyber ranges
collaboratively by the White Team and the Green
Team (Yamin et al., 2020). Step 1 and Step 2 fall under
the training scenario design activities typically carried
out by the White Team. Step 3 and Step 4 are part of
environment configuration and the management of
training scenario execution typically carried out by the
Green Team. Finally, Step 5 is carried out as part of
learning activities including tutoring, scoring and
analysis of scoring carried out by the White Team.
As explained in Section 5, there are several
approaches that use some form of attack modelling
for the purpose of designing and executing training
scenarios. In our approach, we use the CORAS risk
modelling language. CORAS has been empirically
shown to be intuitively simple for stakeholders with
different backgrounds (Solhaug & Stølen, 2013).
CORAS is also based on international standards like
ISO 27005 and ISO 31000, which means that the
language supports well known and widely used
cybersecurity concepts (see Section 4). In the context
of cyber ranges, it is expected that the White Team
and Green Team are familiar with concepts such as
threat scenario, vulnerability, unwanted incident, etc.
With respect to the technical aspects of our
method considering the development of R scripts
based on the cyber-risk models, this is an activity
expected to be carried out by the Green Team as part
of environment configuration. However, the White
Team on the other hand can support the Green Team
by explaining the logic of the expected cyber-risk
assessment algorithms. These activities are thus in
line with the expected roles of White Team and Green
Team members (Yamin et al., 2020).
A threat to validity in terms of the generality of
our method is that the method has been applied only
on our cyber range architecture described in Section
3. However, according to the taxonomy provided by
Yamin et al. (2020), we see that the architecture of
our cyber range is in line with general cyber range
architectures covering components within scenario
development, environment setup and configuration,
monitoring, teaming (red, blue, white, green),
learning (training material), and management of the
cyber range. It is therefore a straightforward task to
map our method to existing cyber range architectures,
in order to apply our method in different cyber ranges.
At the time of writing, our method has been tried
out in the context of the CYBERWISER.eu project,
within three large scale pilots (cases) from three
different domains: academia, transport, and energy.
Although the stakeholders in these pilots come from
different domains, they all are cybersecurity experts
who took the role as White Team, while the technical
team of the project took the role as Green Team. All
steps of our method were carried out and tried out in
order to train students (in the case of the academic
pilot) and security staff (in the case of the transport
and energy pilot). More empirical studies of our
ICSOFT 2020 - 15th International Conference on Software Technologies
518
method are planned (CYBERWISER.eu, 2019b).
However, the fact that all steps of our method were
successfully carried out collaboratively in different
domains with people from various background,
supports the feasibility of our method in real world
cyber range applications.
Thus, from a methodological point of view, it is
reasonable to argue that our method is easy to use and
comprehensible by scenario developers.
6.2 Fulfilment of Success Criterion 2
The second criterion states: The method must provide
necessary guidelines to create cyber-risk models that
facilitate real-time evaluation of participants.
The first step of our method provides detailed
explanation on how to develop cyber-risk models
based on a training scenario description, how to
identify indicators to capture the dynamic behaviour of
the training scenario, and finally translate the cyber risk
models with indicators to machine readable risk
assessment algorithms. These risk assessment algo-
rithms are used to assess the risk level of an ongoing
cyber-attack in a training scenario in real-time. From a
team perspective, these algorithms are used to assess
how well the Red Team is carrying out an attack in
real-time (the higher the risk level the better).
The second step of our method explains how to
identify risk countermeasures by creating CORAS
treatment diagrams. These countermeasures are
implemented in the cyber range and made available
to the Blue Team during a training scenario as risk-
countermeasure options. Based on the selected
countermeasures in a training scenario, the risk level
may go down. This factor is used to assess how well
the Blue Team is performing in real-time protecting
the simulated infrastructure from exposed cyber-
attacks. That is, the lower the risk level (due to
selected countermeasures) the better the Blue Team is
performing.
To this end, our method provides necessary
guidelines to create cyber-risk models that facilitate
real-time evaluation of participants.
6.3 Fulfilment of Success Criterion 3
The third criterion states: The method must produce
useful feedback to the participants in terms of
exercise evaluations.
As explained in Section 4.5, our method supports
the evaluation of participants carrying out training
scenario exercises on cyber ranges. The novel
contribution of our approach relies on the automation
of the evaluation process, using information collected
during the exercise to provide automated evaluation
in real-time.
The evaluation process in our approach takes as
input the cyber risk exposure of the cyber-attack
considered in the training scenario, flags logging the
achievements of the participants, logs from monitoring
tools, and results from vulnerability scans as explained
in Section 4.5. In order to capture evaluation carried
out manually by the White Team, complementing the
automatic evaluation, our approach provides a
questionnaire to be filled out by the White Team based
on their expert knowledge and observations.
According to Yamin et al. (2020), current cyber
range approaches mainly use scoreboards in which
progress of participants is presented based upon the
task they completed. Our approach also provides
scoring in terms of grading. However, compared to
existing approaches, our approach contributes with
additional evaluations in the sense that the
Performance Evaluator provides a report in which
detailed information about strengths and weaknesses
of the participant's cybersecurity skills are presented,
including indications on how the participant may
improve weaknesses. Our initial experience is that the
participants find this evaluation very useful in order to
plan further customized cybersecurity learning
activities.
7 CONCLUSIONS
In general, there is an urgent need for highly skilled,
multi-disciplined cybersecurity professionals, and at
the same time there is an awareness gap and lack of
integrated training modules on cybersecurity related
aspects on all school levels. In order to address this
need and bridge the awareness gap, we have
developed a method to train and evaluate the
cybersecurity skills of participants in cyber ranges
based on cyber-risk models.
The target users of our method are the White and
Green Teams typically considered in cyber ranges.
The method uses cyber-risk models to support the
design and execution of training scenarios. The
output of our method is a performance report for the
participants being trained, that is, participants in the
Red or Blue Teams. The report provides detailed
information based on the executed training scenario
exercises reporting strengths and weaknesses of the
participant's cybersecurity skills, as well as directions
for improving the weaknesses.
We have applied our method in three pilot cases
from academia, transport, and energy. Our initial
results indicate that the method is easy to use and
An Approach to Train and Evaluate the Cybersecurity Skills of Participants in Cyber Ranges based on Cyber-Risk Models
519
comprehensible for training-scenario developers
(White and Green Team), develops cyber-risk models
that facilitate real-time evaluation of participants in
training scenarios, and produces useful feedback to
the participants (Blue and Red Team) in terms of
grading and detailed evaluation of strengths and
weaknesses regarding cybersecurity skills.
As next steps, we will carry out empirical
evaluations focusing on user experience in the
abovementioned large-scale pilots and based on our
findings continue improving our method.
ACKNOWLEDGEMENTS
This work has been conducted as part of the
CYBERWISER.eu project (786668) funded by the
European Commission within the Horizon 2020
research and innovation programme.
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... However, this is covered in our previous work where we explain how to develop training scenarios on cyber ranges based on cyber-risk models (G. Erdogan et al., 2020a). Developing hands-on training exercises for cyber ranges is therefore not covered in this paper. ...
... The training materials developed are packaged into SCORM files and then integrated in our online cyber range platform which we have reported in earlier work (Basile, Varano, & Dini, 2020;G. Erdogan et al., 2020a). Our cyber range platform makes use of Moodle, which is an open-source learning platform, to host a course. Figure 3 is a screenshot from our cyber range in which we see the view a participant sees when taking a course. In this case, the course shown is Introduction to cyber-risk assessment, which is the course described in Table 3. ...
... SDL is similar to the CTTP Specification Language [5] used in THREAT-ARREST, that allow us to specify the different components of a cyber system. Erdogan et al. [10] introduce a training and evaluation approach based on the CORAS risk models [19] that specify cyberrisk models in order to facilitate real-time risk assessment and evaluation of trainees. Similarly, the definition of the CTTP Models will drive the training process, and align it (where possible) with operational cyber system security assurance mechanisms to ensure the relevance of training. ...
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