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|title=Towards a Human Factors Ontology for Cyber Security
|pdfUrl=https://ceur-ws.org/Vol-1523/STIDS_2015_T04_Oltramari_etal.pdf
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==Towards a Human Factors Ontology for Cyber Security==
https://ceur-ws.org/Vol-1523/STIDS_2015_T04_Oltramari_etal.pdf
Towards a Human Factors Ontology
for Cyber Security
Alessandro Oltramari Diane Henshel & Mariana Cains Blaine Hoffman
Carnegie Mellon University Indiana University Army Research Laboratory
Pittsburgh, USA Bloomington, USA Aberdeen, USA
Abstract— Traditional cybersecurity risk assessment is order to enable cybersecurity risk assessment to become
reactive and based on business risk assessment approach. The more predictive, the process and models need to incorporate
2014 NIST Cybersecurity Framework provides businesses with humans and risk factors together in a single model and use
an organizational tool to catalog cybersecurity efforts and metrics that go beyond the direct assessment of classical
areas that need additional support. As part of an on-going
vulnerabilities (confidentiality, integrity, accessibility, or
effort to develop a holistic, predictive cyber security risk
assessment model, the characterization of human factors, CIA).
which includes human behavior, is needed to understand how First, when considering CIA, the actual measurement or
the actions of users, defenders (IT personnel), and attackers evaluation of these vulnerabilities will depend on the
affect cybersecurity risk. Trust has been found to be a crucial situation being modeled. Situations requiring cybersecurity
element affecting an individual’s role within a cyber system. risk assessment can include baseline assessments of network
The use of trust as a human factor in holistic cybersecurity risk protection, but must also include situations in which the
assessment relies on an understanding how differing mental network is being used actively. The actual metrics for, say,
models, risk postures, and social biases impact the level trust protection of an SQL database containing personal
given to an individual and the biases affecting the ability to
information (social security numbers, for example) may be
give said trust. The Human Factors Ontology illustrates the
individual characteristics, situational characteristics, and very different than the metrics needed to be assessed when
relationships that influence the trust given to an individual. evaluating risk related to a field operation using radios,
Furthering the incorporation of ontologies into the science of walkie talkies or cell phones to convey information.
cybersecurity will help decision-makers build the foundation Second, other variables beyond CIA may be the relevant
needed for predictive and quantitative risk assessments. risk variables that need to be analyzed in a risk model.
Take, for example, a situation in which information being
Keywords— cyber security, risk assessment, human factors, used, generated in, or relayed by one network needs to be
cyber operations
received in a specific time window either for another
I. INTRODUCTION operation to begin or so that the information can be used
maybe by the human who will receive the information.
A. The Holistic Cybersecurity Risk Framework Within a military or other time critical context, the
The science of cybersecurity risk assessment has been evaluation goes beyond time to access information; it must
reactive, narrow in focus, and based on a business risk include time to act on the accessed information and can
assessment approach. More recently, the National Institute include time for completion of actions within a critical time
of Science and Technology (NIST) responded to the 2013 window. In this example, time to completion of a task is the
“Improving Critical Infrastructure Cybersecurity” Executive critical metric that must be tracked, and so must be
Order with the development of the 2014 NIST incorporated into the risk model.
Cybersecurity Framework [1,2]. The NIST framework aims Third, humans are a part of virtually all networks,
to provide organizations and businesses with best risk whether as users, defenders (and IT personnel) or attackers.
management practices that can be implemented to improve All humans can introduce risk into the network, not just
the security and resilience of critical infrastructure. NIST attackers, a consideration acknowledged when users are
recognizes that risk management is an iterative process of asked how they use the system (and system components) as
risk identification, risk assessment, and risk mitigation. part of the NIST risk management and risk assessment
While the NIST framework provides businesses and process. Defenders or IT personnel can also increase cyber
organizations with a neatly organized account of their risk if they are, for example, less skilled, or tired, or inside
cybersecurity efforts, the framework fails to capture the threats. Humans can also reduce risk in a cybersecurity
concept that humans are an inherent risk to any system in system. Defenders put in place baseline protections, and
which they directly or indirectly participate. then track attacks on the system to assess whether the
To go beyond the current risk framework promulgated protections have been breached and what needs to be done
by NIST [1,2], risk assessment needs to be more holistic. In to increase system hardening (protections), counteract
STIDS 2015 Proceedings Page 26
malware that may have introduced access to the system (or risk, conceived as “the probability that a successful attack
otherwise compromised the system and system assets), and occurs”, which clearly fails to account for the mutual
repair damage to the system. Users can decrease risk by dependence between profiles of attackers, system
being aware of (and not being hooked by) spam or phishing vulnerabilities, level of expertise of the defenders,
efforts, ensuring their personal system assets are monetization of information loss resulting from data
appropriately protected, and by not downloading infected breaches, etc. In general, a too-coarse representation of risk
files or accessing malware-linked websites. Therefore, is a pervasive problem in the state of the art on ontologies of
human-dependent metrics must be included in a holistic risk cyber security: it’s the case of [6] and [7] where the in-depth
analysis of cyber security. conceptual distinctions adopted to model cyber attacks are
A fully predictive cyber security risk assessment model not matched by a corresponding level of detail in defining
will take into account humans as risk factors, and as risk cyber threats and risk assessment procedures.
mitigators, and will enable the incorporation of metrics that The most popular modeling solution in risk-related
go beyond the classic CIA vulnerabilities. In order to ontology research seems to be the reification of risk-
develop such a model, we have been characterizing the assessment and threat-quantification into the process of
universe of cybersecurity by framing the characteristics, ‘rating’, whose attributes are expressed either qualitatively
attributes and, ultimately, metrics that can be use to describe (e.g., by means of high, medium and low dimensions in the
the risks associated with any cyber network. The framework Likert scale) or quantitatively (measuring the probability of a
has multiple pieces, and metrics that are assessed at different risk). Note that in ontology modeling, reification of
levels. properties is commonly adopted as a method to bypass
Three main parts to the Cybersecurity Risk Framework language expressivity limits: in RDF, for instance, a relation
identifies system level metrics, policy related metrics, and with arity n > 2 can be represented with a statement about
asset related metrics. System level metrics are evaluated at those n entities. Thus, for instance, we could represent the
the full system level, such as probability of completion of a fact that a set of n cyber vulnerabilities exposes a system to a
mission or a system level task. Policy level metrics evaluate certain risk factor, by asserting a risk-rating statement about
the risks associated with the policies that govern the those known n vulnerabilities [8]. An alternative approach
network and network assets. Asset level metrics are comes from Enterprise Risk Management (ERM), an area
evaluated at the asset level, such as metrics to assess risks that concerns the identification, assessment and mitigation of
associated with specific machines, a virtual network, or an operational risk: for instance, Lykourentzou and colleagues
operating system. One piece of the asset level framework focus on seven subclasses of events, i.e. ‘Failure’,
characterizes the Human Factors that introduce or mitigate ‘Infrastructure disruption’, ‘Occupational incident’, ‘Fraud’,
risk in a cyber network [3], which is then being incorporated ‘Disaster’, ‘Attack’, binding each of these event types to a
into an ontology. One goal of this framework and ontology wide spectrum of ‘Root causes’ and ‘Treatment plans’ to
is to identify the factors that contribute to a key aspect of address risk factors [9]. ERM’s approaches can be effective
human-related cyber risk, trust. not only to identify risk-related event patterns, but also to
elicit the behavioral patterns in the adoption of risk
management practices. In this context, ontologies supply an
B. An ontological approach to risk modeling
axiomatic infrastructure to mental models of risk-related
A recent report on quantification of cyber threats patterns.
highlights the intrinsic complexity of the cyber domain [4]: The rest of the paper is organized as follows: Section II
in this document experts pinpoint the bottleneck of cyber makes the case for a holistic approach to risk in cyber
threat assessment on the lack of “standardization and security, introducing the role of trust ontologies; Section III
benchmarking of input variables”, as conversely focuses on the Human Factors Ontology (HUFO); finally,
accomplished – they add – “by the car insurance industry” Section IV draws preliminary conclusions and sets an agenda
(p.16). But if agreeing on the meaning of notions like ‘age’ for future research.
and ‘gender’ of drivers, ‘weight’ and ‘year of built’ of cars,
‘claims history’, etc. seems mostly straightforward,
specifying the semantics of concepts like ‘system II. RELATED WORK
vulnerability’, ‘software usability’, ‘trust’, ‘password
strength’, etc. requires advanced technical knowledge, fine- A. Ontologies of cyber security
grained modeling primitives, and non-trivial metrics. The U.S faces cyber attacks by rogue states and terrorist
Little effort has been put into this standardization organizations on a daily basis. While greatly increased use
process. For instance, Fenz and Ekelhart propose an ontology of information systems has contributed enormously to
based on four parts, i.e. security and dependability economic growth, it has also made the U.S. vulnerable to a
taxonomy, the underlying risk analysis methodology, the variety of cyber threats that are difficult to contrast and
concepts of the IT infrastructure domain and a simulation prevent. There are numerous factors that make cyber
enabling enterprises to analyze various policy scenarios [5]. defense, and cyber security in general, especially
Notwithstanding the comprehensive investigation, the work problematic. The kinds of threats are diverse and span a
presented in [5] is affected by an underspecified notion of wide spectrum of private and public interests: destruction or
STIDS 2015 Proceedings Page 27
theft of data, interference with computer networks and based on a probabilistic ontology that detects warnings in a
information systems, disruption of the power grid and cloud computing environment. A generic service-oriented
telecommunications, denial of services, etc. The legal and framework of trust ontologies is described in [18]. A trust
ethical status of cyber attacks or counterattacks by states are ontology aiming at improving the semantic specification of
also unclear, at least when deaths or permanent destruction trust networks in the context of social institutions and
of physical objects does not result. It is still an open ecosystems is discussed in [19]. In [20], the author focuses
question what U.S. policy is or should be, and how cyber on six general areas to derive trust for a system, namely
threats are analogous to traditional threats and policies—for user, hardware, software, network, machines, and the
example whether “first use” deterrence, and in-kind applications, mapping trust associated with each area to
responses apply, and whether a policy of pure cyber defense specific attributes. An ontology-based approach to integrate
does not put the far greater burden on attacked rather than semantic web based trust networks with provenance
attacking nations [10]. information to evaluate and filter a set of assertions is
As these arguments suggest, untangling the complexity presented in [21]. In [22], a reference ontology to develop
of cyber security does not solely depend on pinning down privacy preserving negotiation systems is delineated.
the computational elements into play, but demands a
thorough analysis of the human factors involved. In this
regard, cyber security must be studied in the context of III. THE HUMAN FACTORS TRUST ONTOLOGY
“sociotechnical systems” [11], where the interaction A. The Human Factors Trust Ontology
between people and technology in workplace is central.
Adopting a standard understanding and definition of
Ontology analysis has recently proved to be an effective tool
terms and concepts is a foundational requirement for good
for investigating the defining aspects of that interaction [12].
cyber security practice, owing to the nature of the space and
Informed decisions emerge when a cyber analyst
the need for rapid, efficient decision-making. Cyber security
projects her observations into a broad context that factors in
is an adversarial space, where defenders must project
threat and attack types, space of defensive maneuvers,
possibilities and be ahead of their opposition in order to be
system vulnerabilities, risk assessment and mitigation under
successful. Enacting strategies favors selecting a suitable
time constraints. Obrst and colleagues [13] provide the most
course of action in minimal time over exhaustively
systematic description of a wide-ranging ontology of cyber
searching [23,24]. Furthermore, the data available is not
security, but only a small portion of this large-scale project
always straightforward, requiring collection and parsing in
is devoted to the human component. Various agencies and
order to construct an understanding of the situation(s) at
corporations (NIST [1,2], MITRE [14], and Verizon [15])
hand. Numerous sources of relevant information are often
have formulated enumerations of types of malware,
applicable, including network monitoring tools, logs, system
vulnerabilities, and exploitations: MITRE, which has been
statuses, and hardware monitors. Analysts are situated at the
very active in this field, maintains two dictionaries, namely
center of a large-scale data fusion process, identifying and
CVE (Common Vulnerabilities and Exposure1) and CWE
defining information through patterns and relationships to
(Common Weakness Enumeration2) and a classification of
perceive the ground truth of the cyber systems and assets
attack patterns (CAPEC - Common Attack Pattern
they are defending and monitoring [25,26,27]. Once
Enumeration and Classification 3 ). Regardless of the
collected, the information must be appropriately combined,
important issues covered by these initiatives, they have two
categorized, and communicated in order to provide a useful
major problems: 1) machine-readability is not supported,
and accurate picture of the world on which future strategies
making them ineffectual as computational models of cyber
can be based. Simply stated, cyber defense is heavily
security; 2) the human component is mostly overlooked,
focused on the human analysts and agents involved in a data
making the resulting models partial in scope.
fusion and situation awareness process.
In order to overcome these problems, in the context of Through processing of data, defenders can draw
the Cyber Collaborative Research Alliance we are conclusions and decide how to respond to evolving
developing CRATELO, a three-level modular ontology of scenarios. Implicit within the workload is a desire and
cyber security. In the next section we are going to describe preference for information that can be trusted, a concept that
the general features of CRATELO, focusing on the Human requires a lot of unpacking to properly understand. In fact,
Factors Trust Ontology module (HUFO). conceptualizing trust in order to evaluate its role and
presence within a system is itself a difficult problem; there
are literally hundreds of definitions of trust covering
B. Trust ontologies
interpersonal trust, trust in automation (system trust), and
Ontology-based models of trust have been studied in human-machine interaction [28]. However, that variety only
various domains [16]. In [17], the authors propose an strengthens the argument for constructing and supporting an
intelligent and dynamic Service Level Agreement (SLA) ontological representation of cyber security. The core
similarities of cyber security and the tasks involved are
1
essentially the same [29], which also supports the creation
https://cve.mitre.org/
2
https://cwe.mitre.org/
3
https://capec.mitre.org/
STIDS 2015 Proceedings Page 28
of a standard ontology. Thus we should be able to describe insider access they possess, denoting when this access is
the human factors that influence trust in a way that can be authorized or unauthorized. A person’s situational
applicable regardless of the specific cyber environment or characteristics also influence the knowledge they can access
organization involved and that will help explicate the role of and may influence the attention they bring to a situation. For
trust in risk assessment and evaluation. example, a user who is an executive of a company may have
Assessing cyber security risks is a multi-component, significant authorized access to assets but lack the same
multi-tiered problem that involves hardware, software, level of attentiveness to security concerns and information
environmental, and human factors. Effective and successful that a network analyst possesses. Knowledge and skill
efforts must consider impacts beyond the computer assets characteristics call to attention the experience, expertise,
and network, taking a more holistic approach that considers and situational awareness capabilities of the individual,
the users, defenders, and attackers involved [3]. Exploring including demographics such as years working in a position
the differences among human roles and human factors and training as well as their proficiency with relevant tools
includes exploring how trust permeates risk assessment, and techniques. Behavioral Characteristics are split into
such as trust in information, in people, or in security spaces such as motivation, rationality, malevolence vs.
policies. Information is not uniformly trusted and benevolence, and integrity. For example, a defender who is
incorporated into situation awareness and defender rational, benevolent, and has a record of following through
responses automatically, but it is built over time as those with work and being accountable for his or her
involved develop relationships, progress through training, responsibilities will likely exhibit persistence in defending
and gain experience [30]. Individuals grow trust in one assets and building appropriate situational awareness. We
another through working together, and people gain trust in have expanded the framework to include traits that influence
systems as they continue to demonstrate consistent behavior. the behavioral characteristics, including ideology, ethical
Previous definitions of trust aggregate characteristics into a attributes, risk averseness, and personality traits. Each of
whole sum, including concepts such as competence, these may scale the behavioral characteristics in some
benevolence, integrity, predictability, attitude, intention, fashion or serve as the driving force behind a person’s
behavior, reliability, dependability, and faith [31] [32] [20]. integrity, benevolence, or rational approach to cyber
The human factors trust ontology aims to map these security situations. Collectively, these characteristics and
concepts into understood and explicit relationships that tie traits impact the individual’s interactions with mission
together risk assessment across the human and human- assets and play a role in determining risk. For example,
system interactions within the cyber security space. defender with poor motivation and integrity, insufficient
As part of an ongoing development of holistic cyber knowledge, and appropriate insider access can present a
security risk assessment, we have been creating a higher risk, whereas an attacker with high motivation and
framework that enables predictive and proactive defenses knowledge despite limited insider access also poses higher
[33,34,35]. A critical component of this process has been the risk.
characterization of human factors, such as trust, and Trust also comes through across these spaces. The
mapping the relevant risk attributes to the risk spaces predictability and reliability of an individual generates a
involved in cyber security. Overall, this is a process of sense of trust in his or her actions and creates a reputation
creating, enumerating, and solidifying risk characteristics for that individual. The expertise and knowledge possessed
and factors, and in many cases refining them and relating can instill a faith or confidence in the work a defender will
them to the human factors. The latter are broken into three do, and users with sufficient integrity will be trusted to
main categories of attacker, defender, and user with a shared follow security policy and not act maliciously within the
core of spaces (their behavioral characteristics, knowledge network. In effect, the human factors of trust directly
and skill characteristics, situational characteristics, and associates with risk evaluation of cyber situations, and we
traits that influence behavior) that create the definition of can explore the relationships across the human factors of
each [3]. The framework (see Figure 1) can be navigated cyber security to discover where risk manifests and how
from top to bottom, the lower tiers breaking out into the trust is generated and influenced. Integrating the human
more specific metrics and concepts that, collectively, factors framework into a cyber security ontology provides a
describe and detail these core spaces, which allows for the logical means to explicate relationships both obvious and
mapping of attributes to measures and data that can be used unintuitive, follow their connections, and evaluate trust’s
to create risk evaluations. presence and impact on the risk present within a given
Situational Characteristics focus on where in the network.
system/network the individual is positioned and the level of
STIDS 2015 Proceedings Page 29
Figure 1 – Trust Framework of Human Factors in Cyber Security.
Figure 2 – A visualization of HUFO.
has been originally formulated by [39] and subsequently
B. HUFO and Trust: an overview
formalized in DOLCE ontology [37]. Intuitively, a quality
HUFO (see Figure 2 above) is part of CRATELO [36], a corresponds to an individual attribute of a specific entity, as
suite of integrated ontologies of cyber security, designed on ‘predictability’ or ‘reliability’ can be considered attributes
the basis of DOLCE top level [37], extended with a of ‘trust’; a quality space is the abstract representation of an
security-related middle ontology. These top, middle and attribute’s semantics, e.g. a boolean space that denotes the
domain level ontologies currently add up to 330 classes, ‘reliable/unreliable’ dichotomy. An important topological
connected by 162 relationships (132 object properties and property of quality spaces is that their dimensional structure
30 datatype properties) and encoded in OWL-DL. The can vary. For instance, the ‘reliability space’ can be more
logical expressivity of CRATELO is SRIQ, a decidable complex than a bidimensional configuration: in particular,
extension of the description logic SHIN (for more details this is the case when reliability is conceptualized as
see [38]). probabilistic distribution between maximum reliability
The relation holding between the human factors and the (100%) and complete unrealibility (0%). The atomic parts of
metrics used to assess them is captured by the semantic a quality space, which collectively denote the range of
characterization of ‘qualities’ and ‘quality spaces’, which
STIDS 2015 Proceedings Page 30
values used to specify an attribute’s semantics, are called support of risk assessment and risk prioritazion in cyber
‘quality regions’. Note that quality regions of a linear space operations.
reduce to points. The semantic model outlined in this paper is only a first,
As mentioned above, ‘predictability’ and ‘reliability’ are preliminary step in the process of porting a larger model of
conceived in HUFO as components of ‘trust’, a complex the cyber security ecosystem into a computational ontology.
factor that is influenced by inherent and external The holistic nature of our approach makes the task
characteristics, in combination with measures of human exceptionally challenging and, to the best of our knowledge,
performance in a given situation. Hence, trust is not only uniquely systematic in cyber security research. Despite the
associated to human characteristics, but emerges as an complex problems we are trying to solve, we’re also
essential aspect of sociotechnical systems: the hybrid nature convinced that, in the forward-looking vision of the ARL
of trust is particularly evident in the cyber security domain, Cyber Security Collaborative Research Alliance, our
where a trustworthy interaction with computer network approach sets a realistic and crucial milestone toward the
systems is the ‘conditio sine qua non’ for a foundation of a science of cyber security.
defender/attacker to accomplish a mission in cyberspace4.
Figure 2 represents an overview of HUFO generated
using OWLGrEd5: the purple links represent subsumption ACKNOWLEDGMENTS
relationship between classes, whereas the dotted arrows This research was sponsored by the Army Research
indicate either the ‘component-of’ or the ‘influenced-by’ Laboratory and was accomplished under Cooperative
property (textual labels in the figure disambiguate the Agreement Number W911NF-13-2-0045 (ARL Cyber
equivalent graphical notations); classes are depicted as Security CRA). The views and conclusions contained in this
yellow boxes, instances as green boxes. The object property document are those of the authors and should not be
‘component of’, holding between attributes and qualities, is interpreted as representing the official policies, either
modeled as a generic ‘part-of’ relation [40], whereas the expressed or implied, of the Army Research Laboratory or
‘influenced-by’ relation reflects DOLCE’s characterization the U.S. Government. The U.S. Government is authorized to
of general dependence, to highlight the strong connection reproduce and distribute reprints for Government purposes
between the assessment (existence) of proper internal and notwithstanding any copyright notation here on.
external characteristics and the computation of the derived
trust level. Note that objective, subjective, and objective-
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