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    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>Using a socio-technical systems approach to design and support systems thinking in cyber security education</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Erjon Zoto</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Stewart Kowalski</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Edgar A. Lopez-Rojas</string-name>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Mazaher Kianpour</string-name>
          <email>mazaher.kianpour@ntnu.no</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Norwegian University for Science and Technology, NTNU Gjøvik</institution>
          ,
          <country country="NO">Norway</country>
        </aff>
      </contrib-group>
      <fpage>123</fpage>
      <lpage>128</lpage>
      <abstract>
        <p>Information security (IS) has been categorized as protecting the confidentiality, integrity, availability, authentication and accountability of information. There is a gap between what companies and institutions plan to do while developing their internal IS-related policies and what it should be done according to a system perspective in this area. Our task as researchers is to bridge this gap by offering potential solutions. The aim of our work is to promote the usage of a socio-technical systems (STS) approach to support the emerging role of systems thinking in cyber security education using simulation as a supporting tool for the learning. Meanwhile, new trends in cyber security curricula suggest an important shift towards new thinking approaches to be used, such as systems thinking.</p>
      </abstract>
      <kwd-group>
        <kwd />
        <kwd>Socio-technical systems</kwd>
        <kwd>information security</kwd>
        <kwd>cyber security</kwd>
        <kwd>systems thinking</kwd>
        <kwd>simulation</kwd>
        <kwd>modeling</kwd>
        <kwd>education</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>-</title>
      <p>
        We hardly pass any day without hearing of new cyber security incidents. With all these
vulnerable systems and threat actors out there, organizations today are in a constant
race to defend adequately against potential cyber-attackers through technical or social
means. A properly educated and aware staff has been identified as one of the most
cost-effective means to keep your organization ahead in the race, as in [
        <xref ref-type="bibr" rid="ref1">1</xref>
        ].
      </p>
      <p>
        In order to improve the cyber security education of the Information Technology (IT)
staff, the Joint Task Force on Cyber security Education (JTF), a worldwide research
group, was established to develop comprehensive curricular guidance in cyber security
education. The JTF has produced just recently a new curricular volume that focused on
the new thinking processes, namely adversarial and systems thinking [
        <xref ref-type="bibr" rid="ref2">2</xref>
        ].
      </p>
      <p>The aim of this poster paper is to present our ongoing work using a STS approach to
model and build a simulation-based teaching tool in “Adversarial and Systems
Thinking” to raise the awareness towards cyber security of students participating in a Master
Program in Information Security.</p>
      <p>
        The ongoing modeling work is based on a combination of theoretical models [
        <xref ref-type="bibr" rid="ref3">3</xref>
        ] and
data from real-world reported cases about cyber-attacks news1. In the simulation case,
we present a scenario where attackers with diversity in skills and motivations try to
break into different objectives from states to corporations, while defenders use their
skills and resources to stop and deter the attacks. The learning objective of the simulator
is to indicate students the relevance between different conditions that make a
cyberattack and a cyber-defense effective.
2
      </p>
    </sec>
    <sec id="sec-2">
      <title>Background</title>
      <p>
        A socio-technical system can be seen as being composed from two components: the
social and the technical [
        <xref ref-type="bibr" rid="ref4">4</xref>
        ].
.
As Figure 1 shows, each of the components can be broken down in two subcomponents.
The social component has its cultural and structural subcomponents, while the technical
side has its own machines and methods as subcomponents. We have used the same
approach when designing a simulator dealing with cyber security issues.
      </p>
      <p>
        Pastor et al. [
        <xref ref-type="bibr" rid="ref5">5</xref>
        ] have done an extensive research work on the available state-of-the
art simulation tools that can be used on the purpose of teaching and training. They
suggest that such simulation tools should be designed to have a user-friendly interface and,
at the same time, allow the user to obtain a deep understanding of the concepts.
      </p>
      <p>We believe that modeling and simulation create a good and efficient way to produce
data that can be mapped to real cases of cyber events. The modeling phase purpose is
to create a normalized view of the cyber security situation, while the simulation phase
allows the imitation of typical attack activities against a specific infrastructure, with
specific security controls in place, grouped in sets of possible scenarios.</p>
      <p>
        We built the tool in Netlogo [
        <xref ref-type="bibr" rid="ref6">6</xref>
        ], inspired from a relevant work in the same area from
Ben-Asher and Gonzalez [
        <xref ref-type="bibr" rid="ref7">7</xref>
        ] and a study prepared from Ponemon Institute [
        <xref ref-type="bibr" rid="ref8">8</xref>
        ], while
developing their works further by introducing the STS approach within our tool.
      </p>
      <p>Ben Asher and Gonzalez came up with a simple cyberwar game that takes place in
a network of n players. Each player has two main attributes, Power and Assets. Power
represents the player’s cyber security infrastructure, seen also as the investment in
cyber security, while Assets entail the confidential information available for use.
1 https://thehackernews.com/2017/09/apt33-iranian-hackers.html</p>
      <p>The Ponemon Report showed the relationships between the time spent and
compensation of today’s cyber attackers and the way that organizations can thwart attacks.
Some relevant findings were the average cost of $1,367 on a yearly basis for the tools
that an attacker needs to execute his attacks and the average time spent against different
target security infrastructures, ranging from 70 up to 209 hours on average.</p>
      <p>In the next section we will explain how we used the STS approach for the tool.
3</p>
    </sec>
    <sec id="sec-3">
      <title>Designing the simulator with a STS approach</title>
      <p>We started designing the simulator by thinking that Defense or Attack actors in a
potential cyberwar can be represented by their own socio-technical systems. Actors will
have their own culture - defined by certain values, traditions and laws, along with a
certain structure - the actor’s position in an organization or the whole society. They
also have a certain level of access to the infrastructure already built (machines) and,
depending on the former abilities and their will or cultural background, they can use
some or other available tools (methods) compared to their colleagues or potential
opponents. Moreover, the type of infrastructure and tools in use should depend on the
attitude of the actors or the structures above them regarding the amount of investments
made while being part of the cyberwar.</p>
      <p>Following the reasoning above, we defined three attributes that could explain the
behavior and performance of the actors in the agent-based simulation tool. The
attributes were Resources - the budget related to cyber activities, Skills - level of training,
literacy and awareness on cyber events, and Motivation - the level of self-motivation
and incentives in a certain time.</p>
      <p>
        We used various sources of data for Resources, including [
        <xref ref-type="bibr" rid="ref8">8</xref>
        ], while we used the GCI
Index, [
        <xref ref-type="bibr" rid="ref9">9</xref>
        ], for the Skills units. We did not make use of any relevant literature on
Motivation, but we are willing to conduct it in the future stages.
      </p>
      <p>Resources are most important when dealing with the technical component, spread
equally between machines and methods for both attack and defense, and somewhat
relevant when dealing with the structural subcomponent, in the process of allocating funds
to different strategies applied.</p>
      <p>Skills are mostly related to the social component, almost equally spread between the
cultural and structural subcomponents, and somewhat relevant to the methods used.
Motivation is generally related to the cultural background, but it can also be affected
from the structural subcomponent, depending on the direct link within the different
levels of management. Motivation, depending on the provided incentives, can lead to the
intentional or accidental misuse of machines. Both Skills and Motivation are slightly
biased towards culture in the social component. Figure 2 depicts this type of
relationship between each attribute and the STS subcomponents, where attributes are located
and weighed according to the reasoning above.</p>
      <p>
        The current version of the simulator allows the user to define initial number of agents
in each side of the battlefield and also the initial value for each of the attributes for all
agents on each side. The user can choose in a range of [
        <xref ref-type="bibr" rid="ref1">1, 100</xref>
        ] for the number of agents
on each side, along with initial units of Resources and Motivation, and [
        <xref ref-type="bibr" rid="ref1">1, 93</xref>
        ] for the
Skills attribute. Figure 3 shows a screenshot of the current version of the simulator’s
interface.
      </p>
      <p>
        The simulator performs each run in a period of max 120 ticks. Each tick represents
a fixed period of time of 3 days, mapping the minimum time required for an attacker to
perform a successful attack [
        <xref ref-type="bibr" rid="ref8">8</xref>
        ], thus making it able to predict the behavior of agents on
both sides within a year. The current version allows a random attack agent to attack one
or more random defense agents in each tick, only if the former combined product of
attributes’ units is at least a third of the combined product of attributes’ units of the
latter. That means that an attack agent should finish the attack in 3 ticks or less,
otherwise it will quit the attack and target another opponent.
      </p>
      <p>If the attack is performed, the defense agent loses some Resources units, based on
the relative power that they have compared to the attacker, taking in consideration the
total amount of combined products between them. The successful attack agent gains
the Resources units lost from the defense agent, while Skills units are also updated by
increasing values in both sides, with the defense agent having a larger increase in terms
of learning experience. Motivation is also updated on the attack agent’s side, increasing
the units by the value of the relative power.</p>
      <p>If the attack is avoided, only the Motivation units are updated on the defense agent
side, by the same value of the relative power.</p>
      <p>Continuous successful attacks can actually decrease one defense agent’s Resources
units towards reaching zero. When this happens, the defense agent goes “offline”,
meaning he does not interact anymore with the other agents.</p>
      <p>According to the assumptions above, the current simulator runs typically end in not
more than 10-12 ticks out of a total of 120 ticks, depending also on the initial values.
Thus, in the current version the attack agents mostly outscore the defense ones.
4</p>
    </sec>
    <sec id="sec-4">
      <title>Preliminary Result</title>
      <p>The simulator was used for the first time this spring in a course entitled
Socio-Technical Enabled Crime. This course is an elective course in a 2 years Master Program in
Information Security. Eight students responded surveys and used the simulator in order
to provide their overall appreciation as related to learning adversarial and systems
thinking.</p>
      <p>Surveys results indicate that most of the respondents expected the simulator could
help them develop their understanding of adversarial and systems thinking. The most
important finding is related to the question on the most relevant attributes that would
affect the chances of defense agents to avoid attacks until the end of the run. In the
presimulation survey, the respondents expected that the most relevant parameter would be
the defense Resources, followed by defense Skills and then Motivation. However, after
trying the simulator, the respondents answered that defense Motivation was the most
relevant parameter, followed by defense Skills and then attack Motivation parameter.
This shift from defense Resources to defense, and especially attack, Motivation, shows
that, at least from the preliminary results, the simulator was able to change the
respondents’ way of thinking.
5</p>
    </sec>
    <sec id="sec-5">
      <title>Conclusions and Future work</title>
      <p>In our poster, we presented how a STS approach can be used to design and support an
agent-based simulation tool, in order to introduce the emerging role of systems thinking
in cyber security education. We defined three main attributes, namely Resources, Skills
and Motivation, affecting the behavior and performance of each actor within the
simulation.</p>
      <p>
        In the future stages, based on the STS approach, we intend to go deeper into the
Motivation attribute, by conducting a more detailed literature review on the theories
explaining attack actors’ motivation, such as the ones related to the MOMM’s taxonomy [
        <xref ref-type="bibr" rid="ref10">10</xref>
        ],
and other theories explaining defense actors’ motivation, such as the protection
motivation theory [
        <xref ref-type="bibr" rid="ref11">11</xref>
        ].
      </p>
      <p>We will use the same approach to analyze and interpret findings from current and future
versions of the designed tool to argue about the benefits of using STS in this area.</p>
    </sec>
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