2023

Mark Quinlan

# The aesthetics of cyber security: How do users perceive them?

<EMAIL_ADDRESS>Aaron Ceross<EMAIL_ADDRESS>Andrew Simpson
<EMAIL_ADDRESS>Department of Computer Science, University of
Oxford, Wolfson Building, Parks Rd, Oxford, OX1 3QD, Oxfordshire, United
Kingdom

###### Abstract

While specific aesthetic philosophies may differ across cultures, all human
societies have used aesthetics to support communication and learning. Within
the fields of usability and usable security, aesthetics have been deployed for
such diverse purposes as enhancing students’ e-learning experiences and
optimising user interface design. In this paper, we seek to understand how
individual users perceive the visual assets that accompany cyber security
information, and how these visual assets and user perceptions underwrite a
distinct _cyber security aesthetic_. We ask, (1) _What constitutes cyber
security aesthetics, from the perspective of an individual user?_ and (2) _How
might these aesthetics affect users’ perceived self-efficacy as they
informally learn cyber security precepts?_ To begin answering these questions,
we compile an image-set from cyber security web articles and analyse the
distinct visual properties and sentiments of these images.

###### keywords:

cyber security, aesthetics, visual learning

##  1 Introduction

Visual media, like illustrations lin2018impact  and diagrams hattwig2013visual
, have accompanied text since the very first written documents
nichols1858illustrations , providing clarification, communicating distinct
emotions or opinions, and serving more subversive ends like propaganda
marland2012political ; cooper2008war ; meyer2008aesthetics . Today, digital
technologies have introduced new types and ways of accessing visual media
while rapidly integrating media consumption into the daily lives of large
segments of the global population david2010impact . Still, contemporary online
news articles and blog posts are often accompanied by visual media that serve
many of the same communicative purposes as those in the earliest human
documents mitchell2005just .

The fields of usable security and cyber security have used visual media to
support learning. For example, user interface designers used (and later
abandoned) skeuomorphism to generate easily identifiable visual objects that
could help users to navigate new interfaces page2014skeuomorphism ;
curtis2015rhetoric , and they frequently use colours to draw attention to
salient information and features within e-learning platforms
tharangie2008kansei ; reyna2013importance . Meanwhile, usable security experts
explore how visual cues can aid users outside of formal learning environments
— that is, how and when visual media can facilitate informal learning.
Informal learning is the primary way in which adults learn about the world
around them malcolm2003interrelationships ; ollis2011learning , and it
typically occurs when individuals actively choose to seek out new ideas and
advice.

Understanding how visual media work to support communication and learning is a
complex task, and it is divided amongst many scholarly disciplines. First and
foremost amongst these is the ancient philosophical branch of aesthetics,
which concerns itself with the nature of perception, taste, and the values of
sensory qualities (e.g., beauty). In the context of this paper, aesthetics
entail the perceptual logic that allows individuals to instinctively analyse
meaning and appraise quality / truth when consuming visual media — whether as
stand-alone objects, as part of a user interface, or as an accompaniment to
text. By extension, learned aesthetic preferences may influence how
individuals navigate information or environments flavian2009heuristic ;
joshi2011aesthetics ; shires2020cyber ; carroll2021usable , and they may play
a significant role in informal learning.

There is a body of literature within the computer science usable security
field that looks at users’ aesthetic perceptions of technology. For example,
work by Fogg et al. fogg2009behavior  found that almost half of all users used
aesthetic judgements to infer the credibility of a site’s content. Compounding
these results, Robinson et al. robinson2020digital  found that individuals use
aesthetic information to make rapid judgements about content, and Alsudani and
Casey alsudani2009effect  reported that these judgements occur within about
3.5 seconds. In the sub-area of cyber security aesthetics, ma2006cyber
outlined various cyber security visualisation techniques, and shires2020cyber
looked at the adaptation of neo-noir aesthetics in cyber security visual
media. There is also work within the field of digital exhibits bernal20201
and the transfer of security aesthetics into cyber security
ghertner2020futureproof . Taken together, these precedents suggest that cyber
security aesthetics can serve as a pedagogical tool, helping users to parse
information and act upon it. Thus, improving our understanding of these
aesthetics could help us to improve the efficacy of security advice
dissemination.

In this paper, we explore the following research aims:

  1. 1.

_What cyber security aesthetics consist of, from the perspective of an
individual user._

  2. 2.

_Provide an explorative discussion as to the manner in which these aesthetics
may affect users’ perceived self-efficacy as they informally learn cyber
security precepts._

To do so, we report on how we assembled an image-set of cyber security images
that reflects what a user typically sees within an informal learning
environment. The corpus spans 1,027 images and is derived from English
language news and online magazine articles from the United States, Canada, and
the United Kingdom. The images are organised into several classes, which we
derived by extracting visual information from the raw images and mapping them
to semantically meaningful keywords, and performed a colour similarity
analysis.

The remainder of the paper is organised as follows. In Section 2 we provide
the background to, and the motivation for, the work described in this paper.
In addition, we define some of the terms of interest. In Section 3 we describe
the process used to create the image-set, as well as the data cleaning process
we used to develop the image-set into usable images. In Sections 4 and 5 we
present and discuss our results, placing them in a broader context. Section 6
presents potential research directions for the broader research community.
Finally, Section 7 concludes the paper.

##  2 Background and Motivation

In this section we discuss the background to, and the motivation for, the work
described in this paper. As aesthetics is such a broad and sometimes ambiguous
term dewey1934art , we begin by providing an overview of aesthetics research
in Sections 2.1 and 2.2, establishing its relevance for our research aims. We
then consider the potential efficacy of aesthetics for cyber security in
Section 2.3. Section 2.4 then returns to our overarching research aims.

###  2.1 Aesthetics and meaning

In Ancient Greece, aesthetics were first described as a ‘sensation’, or the
ability to interact with external stimuli through our bodily senses
beardsley1975aesthetics . Later, Kant Kant1892-KANTCO-3  espoused the
importance of aesthetics for all human domains, arguing that, without its
sense-making power, data would simply remain chaotic, lacking meaning and
structure.111This could mean that data acquires a certain aesthetic once
created (mathematical aesthetics Kant1892-KANTCO-3 ), or that, to have an
aesthetic perspective, one must assemble data fragments into something
meaningful regardless of outcome Kant1892-KANTCO-3 . However, if aesthetics
help to render a shared sensible reality, as asserted by Kant
Kant1892-KANTCO-3 ; zander2016intuition , then aesthetic perception must be
universal, narrative, and standardised. This brings us into the sphere of
semiotics — the study of signs, symbols, and symbolisation, or of the devices
and practices that help to stabilise meanings.

For the purposes of this paper, we define aesthetics as the perceptual logic
that allows individuals to instinctively analyse meaning in visual media, and
semiotics as the conventionalised meanings arising from this perceptual
process. In other words, where aesthetic objects exist solely for their own
purposes Kant1892-KANTCO-3 , informing perceptible meaning walsh1974aesthetic
; wissenburg2012aesthetic  without requiring a specific meaning to be
understood, semiotic objects contain explicitly built-in meanings, whether
skeuomorphic or otherwise carroll2021usable ; barbosa2021semiotics , and can
enhance the meaning of words they are associated with (for example, as a
compendium to a body of text dewey1934art ).

Both aesthetic and semiotic perspectives remain relevant in contemporary
philosophical discourse, as well as in the practice of cyber security. For
example, doi:10.1080/10350330.2019.1587843  describe how viewers attempt to
derive meaning from key referent objects contained within an image. Insofar as
these objects are universally understood, they may yield what Ranciere calls
‘a shared sense of perception’ sayers2005jacques . Furthermore, in the context
of cyber aesthetics, most interactions at the interface level are directed by
symbols and imagery such as icons, pointers, image thumbnails — which
themselves can contain semiotic objects — and so forth. All of these objects
help to convey a system logic to end users, thus enhancing accuracy and
intuitiveness rudner1951semiotic .

Of course, in a nascent field like cyber security, aesthetic systems have not
necessarily been formalised into stable semiotic resources. As such, we must
not preemptively constrain our analysis to specific image contents, addressing
instead the full spectrum of aesthetic objects relevant to cyber security
communication. In this case, we define these to be (visible) digital image-
objects that may themselves contain semiotically legible signs, and which have
been added as an adornment or supplement to relevant cyber security
literature. Although this definition presents some limitations (discussed in
due course), it allows us to account for the narrative functions of
aesthetics, as well as for its use in learning.

###  2.2 Aesthetics and learning

Because we are primarily interested in how users may interpret cyber security
aesthetics in an informal learning context, our understanding of aesthetics is
informed by contributions such as that of chatterjee2014aesthetic , which
draws from the humanities and sciences to illustrate how aesthetics influence
the choices humans make in their given domains of activity. Earlier work by
carper1975fundamental  went further, identifying aesthetics as one of four
distinct structures human beings use when developing knowledge, the other
three being personal, empirical, and ethical. For Carper, the ‘knowing’ of
aesthetics takes the other three structures and enhances them into a new
understanding, creating meaning from otherwise abstract works
carper1975fundamental . Building on Ancient Greek notions of aesthetics,
keenan2016use  proposed the concept of ‘aesthetic knowledge’, wherein sensory
experiences form embedded relationships with phenomena such as colour and
shape. According to Keenan, when prior aesthetic knowledge is combined with
information (or, in our case, images) from user interfaces and other elements
within a digital experience, users can associate prior meanings with this new
information and thereby generate unanticipated interactions keenan2016use .

Taken as a form of knowledge, aesthetic design can enhance users’ ability to
make effective decisions based on a mixture of intuition and explicitly learnt
knowledge carroll2010designing . We know that people often make decisions
based on intuition rather than analytical inference, ‘sensing’ a correct
choice without being able to offer a logical explanation for it
zander2016intuition ; we may also expect that aesthetic objects can serve to
stimulate this intuition. For example, within human–computer interaction,
supplementary visual assets that convey a feeling of uncertainty or ambiguity
can help individuals to comprehend uncertainty even when it is not explicitly
communicated in words fernandes2018uncertainty . It follows that aesthetic
knowledge will impact knowledge acquisition in any given field, including
fields where many users rely on informally learnt knowledge (such as cyber
security rader2012stories ) or cases where decision makers do not have prior
experience with the given situation zander2016intuition .

###  2.3 Aesthetics and self-efficacy

Clearly, the way in which we are presented with information visually impacts
our understanding of, and subsequent decision making towards, a particular
topic. As such, usable security research, user interface design, and cognitive
psychology theory have sought to better understand how and why users make
aesthetic decisions, and how aesthetic attributes can be designed to achieve
certain ends. For instance, some scholars studying the ethics of technological
development have proposed tools to help designers build fairness and
transparency into digital libraries and interface designs through deliberate
aesthetic planning barbosa2021semiotics . Other researchers have explored how
particular aesthetic / semiotic interpretations of user interfaces can enable
users to complete a given task more efficiently carroll2021usable . This
latter effect is particularly interesting in the context of cyber security,
given the brunt of responsibility that individual users have to bear for
protecting themselves, their devices, and their networks online.

One important concept implicated in users’ decision making is self-efficacy —
a generative capability to organise one’s skill-sets and beliefs towards a
desired outcome bandura1999self . According to self-efficacy theory,
individual users implicitly judge their own ability to cope with a given
situation, thus developing self-efficacy beliefs for a specific domain. These
beliefs inform whether individual users will initiate certain behaviours and
carry them through to successful outcomes maddux1995self ; bandura1999self .
Furthermore, self-efficacy is closely related to motivation: the greater the
challenge a user faces, the more self-efficacy they will need to sustain their
motivation bandura1999self ; stumpf1987self . Because cyber security is
perceived to be both important and complex, users tend to exhibit limited
self-efficacy in this domain (as explained by Herley  and explored by
halevi2016cultural  through psychological and cultural means).

Many people develop some degree of self-efficacy through their identification
with role models: people similar to themselves who display, and thereby make
accessible, certain aspirational attitudes, behaviours, or capacities
ajzen1991theory . For example, bosma2012entrepreneurship  observed that role
models in the media can encourage entrepreneurship amongst their viewers.
Applying these insights to cyber security aesthetics, we may suggest that
researchers can utilise aesthetics to enhance users’ self-efficacy by
providing models, structuring and directing behaviour towards goal setting,
and measuring progress towards these goals carroll2010designing .

###  2.4 Our expectations for this exploration

To summarise, we expect that users acquire an aesthetic literacy when they are
repeatedly exposed to domain-specific content, and that this literacy helps
them to navigate and derive meaning from future content. As per our first
research aim, we aspire to understand the aesthetics (and thus aesthetic
literacies) operative in the domain of cyber security, and so we will
imaginatively replicate the process whereby users develop these literacies —
that is, repeated exposure to the aesthetic objects of cyber security — by
compiling an image-set of cyber security’s primary aesthetic objects, allowing
us to appraise and compare them at once. We have defined these objects to be
images that may themselves contain legible signs, and which are typically part
of a larger piece of content like an online article. As per our second
research aim, we will interpret the resulting aesthetics in terms of their
likely effects for users’ self-efficacy.

##  3 Methodology

In this section we discuss the research design of our study, which proceeded
in five steps:

  1. 1.

developing the image-scraping tool in Python to extract images from structured
data sources;

  2. 2.

configuring a viable search methodology based on common cyber security
terminology;

  3. 3.

cleaning the initial pool of images to yield a usable image-set;

  4. 4.

preparing the labels and resources needed for computational image
classification; and

  5. 5.

performing colour analysis to confirm the internal consistency of each image
class.

###  3.1 Developing the image-scraper

Web scraping is a popular digital research technique that allows researchers
to automatically capture freely available online data — that is, data that
does not require privileged access marres2013scraping  — via the use of
scrapers. Our image-scraper is a simple tool designed to capture images from
pages selected by our search methodology (discussed in Section 3.2). Rather
than incorporate additional system logic to ensure that all images were viable
candidates for analysis, we chose to refine the image-set through subsequent
data cleaning (discussed in Section 3.3).

###  3.2 Deriving relevant images from search terms

To establish the list of search terms needed to guide the image-scraper, we
followed the precedent of schatz2017towards , who used Google Trends to
automatically collect real search terms employed by the target
audience.222schatz2017towards  sought to derive a more precise definition of
security, and so they collected the terms that individuals used to search for
security content. This focus on user-centred definitions excluded the
possibility of replicating the work of humayun2020cyber , who looked at
primary studies undertaken within academia. Instead, we followed the
Systematic Mapping Study protocol presented by kosar2016protocol .

We defined a set of base search terms (for example, ‘cybersecurity’ OR ‘cyber’
AND ‘security’) and then added search terms derived from Google Trends (online
OR advice OR protection OR protect OR prevent OR preventative OR tips OR email
OR social network OR password OR hack OR hacked OR hacking). All search terms
were technology-agnostic — they did not include explicit references to
specific products or services. The image-scraper then returned all images that
corresponded with content that included these terms within the title or body
text. Though not exhaustive, this strategy yields an image-set that adequately
represents operative definitions of cyber security, as actualised by users.
There is, of course, scope for future improvement.

###  3.3 Cleaning the data

The aforementioned search strategy yielded an initial image-set of 4,784
images, which we then subjected to an initial data cleaning based on the
following inclusion / exclusion criteria (to enable consistency):

  * •

The image must be derived from a news or blog article that directly addresses
at least one aspect of cyber security and / or explicitly contains our search
terminology. Blog articles were limited to tutorials, editorials, tool
demonstrations, and discussions of technical reports. Due to the nature of the
assessment and the search methodology, we only retrieved images from English-
language sources.

  * •

The image must be accessible and not hidden behind a paywall or other kind of
lockout mechanism, as these obstacles restrict the amount of text that can be
retrieved, making it difficult to explain why some images were included in a
given article or blog post (that is, the role that the images serve in
relation to the text).

  * •

The image cannot be a corporate logo or advertisement (like the lead slide of
a corporate presentation).

  * •

The image must be at least 360x640 pixels for ease of processing.

  * •

The image must be in either .jpg or .png format.

Applying these criteria, we reduced the initial pool of 4,784 images to 3,757
usable images. We then counted and removed all duplicates333We counted the
number of duplicates to assess the extent of duplication within cyber security
aesthetics. and then down-sampled our images to a standard pixel resolution.
This yielded a final image-set of 1,027 individual images, which we used for
analysis 444This image-set can be found here:
https://huggingface.co/datasets/Quinm101/cyberaesthetics..

Figure 1: The colour distance charts for our image class heat-maps. Similarity
decreases as the x-axis moves from 0 (blue) to 1 (pink).

###  3.4 Classifying the images

The next step in the process involved feature extraction — a form of
quantitative image classification wherein categorising labels are assigned to
images based on specific extracted features. For our image-set, we chose to
begin with object recognition to identify any potential semiotic objects (or
signs) before moving onto semantic categorisation (categorising emotion or
other subjective features).

We utilised a variation of the Bag-of-Words (BoW) model to provide human-
assigned classifications for our image-set. The BoW model is often used in
situations where images require text categorisation but word order is not
particularly important. We based our model on work by csurka2004visual ,
selecting three knowledgeable cyber security researchers to manually locate
dominant interest points in individual images (recreating feature extraction)
and derive labels that represent these interest points. This solution
presented several advantages over an automated set-up, as the expert knowledge
allowed for more concise labelling, the ability to label occluded objects that
would have been missed by automated methods, and the ability to construct a
clearly defined codebook for our classification labels based on prior
expertise. However, the contextual awareness these experts brought to the
labelling exercise may have introduced some biases. This limitation could be
mitigated in future studies by recruiting a wider range of annotators.

###  3.5 Measuring image similarity through colour

To confirm the internal consistency of each image class derived from our
classification process, we utilised Weller and Weastneat’s
weller2019quantitative  quantitative, colour-based method for measuring image
similarity. This involved transforming each image’s pixels into 3D coordinates
to produce a multidimensional color histogram for each image, then using the
earth mover’s distance measure rubner2000earth  to compute the pairwise
distances between histograms. We opted for this method over contour-
recognition for object classification (as used by gupta2019nose ;
waldchen2018plant ) because we had already classified our images according to
their dominant features. Colour similarity measures also allow us to more
confidently make qualitative assessments relevant to our research aims.

Colour similarity heat-maps for each class will be shown later on in this
paper, and can be interpreted through Figure 1. Each heat-map represents the
relationship any given image has to the other images within its class, with
blue cell colours indicating greater similarity and red cell colours
indicating lesser similarity.

##  4 Results

Using the process described in Sections 3.2 and 3.3, we compiled an image-set
that covered a wide swathe of cyber security topics and their associated
aesthetics. Small selections of images from each class are shown in Figures 2
and 3. Through the process described in Sections 3.4 and 3.5, we identified 32
distinct and internally consistent image classes in the image-set. These
ranged from abstract interpretations of networked security to imagery
depicting the binary view of cyber security as an eternal battle between
malicious actors and their victims. However, because most of the images
(80.6%) were concentrated in just ten major classes (as detailed in Table 1),
we restrict our discussion to these classes. We further group these classes
into four broad (but not mutually exclusive) categories: Objects, People,
Places, and Others.

Table 1: The top ten classes in our image-set. Class |  Description |  No.
---|---|---
1. |  Physical traditional security semiotics (such as lock, key, or shield) |  290
2. |  Hackerman archetype |  88
3. |  Non-malicious users of cyberspace |  81
4. |  Digital superpositions over cityscapes or skylines |  72
5. |  Physical-digital hybrid workspaces |  69
6. |  Abstract patterns (such as grids) |  64
7. |  Textual content (such as explicit warnings) |  61
8. |  Wall of code (incoherent or standard programming language) |  61
9. |  Disembodied anatomy interacting with a physical device or digital overlay |  42
10. |  Non-security-related skeuomorphism |  32
Figure 2: A random selection of images taken from classes 1-5. Figure 3: A
random selection of images taken from classes 6-10.

###  4.1 Colour

As one would expect, colour features heavily across all of the classes in our
image-set. If we use the heat-maps to discuss similarity potential across
colour and texture (where radical differences may indicate domain-shift within
the class samek2016evaluating ), then this section concerns itself with the
qualitative use of colour as we see it across the complete image-set.

Where many of the abstract forms seen in Class 6 utilise hues running the
gamut from greens to blues which are almost always contrasted by dark
backgrounds, it is clear from the complete image-set that no ‘universal’
definition or convention on the usage of colour exists within cyber security,
beyond the heavy use of cyan blue555This was already informally known to the
cyber security community, where the cyan blue code #235594 was the most
commonly found colour in a large image-set, but which is not explicitly
referenced to here due to the fact it was not cleaned for duplicates and other
issues, and contained little in the way of search methodology. It can be found
here: https://daylight.berkeley.edu/cybersecurity-imagery/..

All of the heat-maps highlight these similarities in colour. Where we find
points of interest are in Class-1, where colour is used to denote objects as
being of specific importance, ranging from useful to dangerous. We may
contrast this with another aspect of cyberspace, the video game, where
emphasis is placed on the colour of objects with which the player may interact
johnson2017history . This codification of objects again lacks a specific
narrative, and objects that are beneficial often share hues with those
semiotics deemed dangerous.

In between the classes, a specific colour analysis leads to results of limited
immediate utility. Turning our attention to Figure 4, we see that our top-two
image classes share a penchant for blacks, blues and whites.

With this large amount of blacks and darker hues serving as background, we see
in many classes a contrasting effect between the brightly coloured objects,
spaces and vertices. In this manner, we hypothesise that colour may be used to
draw the user’s attention to these objects, which exist in a space with no
other domains to draw inspiration from, similar to video games
johnson2017history . A colour analysis may not allow us to infer further
specifics as to their self-efficacy potential, in which the colours may be
used to deliver a decoding of the messages present in the accompanying textual
content. Instead, we look towards the objects, people, places and other
aspects of the image. In these cases, we find the heat-maps to be of more use
as an accompaniment to assess the credibility of the assertions. Each heat-map
represents the relationship any given image has to the other images within its
class, with blue cell colours indicating greater similarity and red cell
colours indicating lesser similarity.

Figure 4: An example of the most commonly used colours within our top-two
classes, simplified to 4-bit.

###  4.2 Objects

Classes 1 and 10 feature objects associated with physical security, like
locks, keys, and shields (and others identified by ghertner2020futureproof );
objects appropriated from non-security domains, such as cameras; and
skeuomorphic adaptations of real-world objects, like digitised versions of
envelopes. Class 1 contains 290 unique images with extraordinary colour
similarity (as per Figure 5), implying consistent use of similar semiotics and
colour schemes throughout. Class 10 is much smaller, featuring only 32 unique
images, but it is notable for its wider variety of objects and colours (see
Figure 6). Figure 3 highlights some of the images from these classes.

Figure 5: Heat-map highlighting the overall colour differences between images
in Class 1 (Traditional physical-digital security semiotics). Figure 6: Heat-
map highlighting the overall colour differences between images in Class 10
(Non-security semiotics and skeuomorphism).

###  4.3 People

Classes 2 and 3 feature individuals who are implied to be malicious (Class 2)
or non-malicious (Class 3) users of cyberspace. We also include Class 9 within
this group, given its emphasis on human anatomy. Class 2 contains 88 unique
images with significant colour similarity (see Figure 7), which in this case
implies similar compositions — individuals assuming similar stances against
similar (dark) background colours. Class 3 consists of 81 unique images and is
slightly more varied in its make-up (as per Figure 8). Class 9 is the smallest
and most differentiated class in this group, consisting of only 42 unique
images with wider colour discrepancies in the heat-map (see Figure 9). Figure
2 highlights some images from Classes 2, 3, and 9 are shown in Figure 3.

Figure 7: Heat-map highlighting the overall colour differences between images
in Class 2 (Hackerman archetype). Figure 8: Heat-map highlighting the overall
colour differences between images in Class 3 (Non-malicious users of
cyberspace). Figure 9: Heat-map highlighting the overall colour differences
between images in Class 9 (Disembodied anatomy interacting with a physical
device or digital overlay).

###  4.4 Places

Classes 4 and 5 feature specific places related to cyberspace and cyber
security, such as futuristic urban spaces (Class 4) and workspaces (Class 5).
Class 4 contains 72 unique images with significant colour similarity (see
Figure 10), once again implying similar compositions and colour palettes.
Class 5 consists of 69 unique images and is more varied, as can be seen in
Figure 11. Figure 2 highlights some examples from Classes 4 and 5.

Figure 10: Heat-map highlighting the overall colour differences between images
in Class 4 (Digital superpositions over cityscapes or skylines).

###  4.5 Other

Figure 11: Heat-map highlighting the overall colour differences between images
in Class 5 (Physical-digital hybrid workspaces).

Classes 6, 7, and 8 variously encompass imagery of digital patterns,
alphanumeric symbols, and other two-dimensional or abstract representations of
cyberspace and cyber security. Class 6 contains 64 unique images with
reasonable colour similarity (as per Figure 12); though similar background
colours are frequently used to represent mathematically defined patterns and
shapes, there is some variation based on other, supporting semiotic attributes
in this category. Class 7 consists of 61 images that are slightly more varied,
as seen in Figure 13. Meanwhile, Class 8 contains 61 images, and it is the
most diverse of the three (as visible in Figure 14. However, the
representativeness of Class 8’s heat-map is limited by the content of the
images, namely incoherent alphanumerical symbols or programming languages on a
dark background. We expect that the variation in the heat-map reflects the
wide variety of colours used in these different symbols, despite larger
compositional similarities. Figure 3 highlights some examples from Classes 6,
7, and 8.

Figure 12: Heat-map highlighting the overall colour differences between images
in Class 6 (Abstract patterns).

##  5 Discussion

In this section we argue that the aesthetic classes established in the
previous section can help to prime readers’ interpretations of associated
texts in ways that affect their self-efficacy. Insofar as the images that
accompany cyber security texts are created and selected for this specific
purpose (that is, for inclusion in these texts), we may say that the observed
aesthetic trends are more or less deliberate attempts to frame cyber security
in a certain manner. Accordingly, we begin with an analysis of the semantics
of cyber security — the colours, shapes, and devices used throughout the
image-set — to understand how cyber security is being framed. We proceed to
analyse the image classes that feature objects, people, and places. We finish
this section by framing these discussions in light of our research aims and
what we learnt in Section 2.

###  5.1 The visual vocabulary of cyber security

Our image-set suggests that cyber security exists at the limits of traditional
human visibility. Indeed, many of the image classes feature abstractions of
objects, situations, individuals, and landscapes rather than concrete
subjects. This seems consistent with typical evocations of ‘cyberspace’ — a
term coined in 1982 by science-fiction writer William Gibson
gibson1982neuromancer  to designate ‘a new universe’ parallel to the physical
but created by the digital benedikt1991cyberspace  (as encapsulated in the
‘fifth branch’ metaphor utilised by the U.S. military branch2020s ). Although
the term has since become synonymous with global computer networks such as the
Internet, it continues to encapsulate the ‘sublime’ sensations associated with
a new frontier. nye1996american  explained that, when users are introduced to
powerful new technologies (such as cyberspace or a digital system), they
experience a pleasurable yet terrifying sensation that alerts them to the
limits of their reality.

Multiple scholars have explored how aesthetic choices can help to acclimate
users to the new reality of cyberspace ‘environments’.
featherstone1996cyberspace , for instance, argued that aesthetics help to
evoke imagery of life in the domain of cyberspace, while croon1999making
explained that imagery can provide insight into a domain and help to cultivate
a form of spatial awareness within it. Within our image-set, we observed heavy
use of mathematical aesthetics such as concentric arcs, simple and tileable
shapes (such as hexagons, albeit with a higher area-to-perimeter ratio), and
connecting lines. Where these devices construct the shape of cyberspace,
colour establishes its tone. In our image-set, we observed a preponderance of
dark shades and blue hues. This is consistent with the work of
shedroff2012make , who researched the use of colour within the domain of
science fiction. One possible reason for this preference in science fiction
and cyber security is the relative rarity of blues in nature greenspan_2013 ;
because this colour is scarce in our physical domain, it effectively
communicates cyberspace’s distinction from the straightforwardly physical and
natural.

Figure 13: Heat-map highlighting the overall colour differences between images
in Class 7 (Textual content such as explicit warnings). Figure 14: Heat-map
highlighting the overall colour differences between images in Class 8 (Wall of
code).

Of the aesthetic classes identified in our image-set, the ones that most
optimally represent cyberspace as a techno-spatial domain separate from, but
parallel to, our physical reality are Classes 3, 6 and 8, which represent 16%
of our total image-set. These classes deploy the aforementioned aesthetic
tactics most consistently / legibly.

###  5.2 Physical traditional security semiotics

beasley2010persuasive  argue that semiotics researchers are mainly interested
in understanding how individual signs, objects, and concepts coalesce into a
coherent visual narrative. Objects such as locks, shields, and keys appeared
frequently in our image-set, either as digital manifestations of physical
objects or, in rare cases, as physical objects in a hybridised physical-
digital environment. These objects represent 20% of the total image-set, and a
selection can be found in Figure 2.

As these objects often pre-date the development of cyberspace, they may
initially evoke physical, rather than digital, security, being grafted onto
cyber security only later to represent what security might mean in cyberspace.
As is the case with skeuomorphic interface design, wherein new signs or
symbols are developed from prior objects (retaining the original’s ornamental
design cues), the new semiotic interpretation may have superseded the older
meaning. Nonetheless, any theoretical constructs associated with the
originator sign have likely been carried forward kearney2001continental .

According to ghertner2020futureproof , security could be seen as a form of
negation, with security signs suggesting the absence of malicious activity.
The lock is a powerful symbol of security in the real world, the key a symbol
of legitimate authority, and the shield a symbol indicating defense in the
event of an attack. These symbols can also be rendered differently to present
a kind of advanced warning system; imagery of a broken lock, key, or shield
might alert the user to potential security infractions.666It must be pointed
out, however, that we did not come across any broken or damaged semiotic
objects in our image-set.

###  5.3 Hackers: anatomy, gender, and race

Humans were represented in 16.4% of the images in our image-set. Using our
codebook, we assigned each of these images to one of two binary categories:
the malicious hackerman archetype (8% of our total image-set, and just over
52% of our total human representations) or non-malicious users (7.8% of our
total image-set, and just under 48% of our total human representations). An
additional 4% of our image-set contained anatomical images, such as those seen
in Class 9. These anatomical images were largely restricted to two types:
those featuring hands and those featuring eyes.

Our image-set broadly suggests a lack of equal representation in cyber
security aesthetics, and thus a lack of diverse role models. Just under 1% of
our hackerman archetype features feminine-presenting people; all others were
either masculine-presenting (63%), implied to be men, or had their secondary-
sex characteristics obscured (for instance, by a mask). While we do see a
wider variety of individuals amongst the non-malicious users — 63% of these
images contained masculine-presenting individuals, 37% contained feminine-
presenting individuals, and 20% contained people who appeared to be of a non-
white background — this does not necessarily represent the gender or
racial/ethnic background ratio of audiences who engage with cyber security
content, but that of the organisations that produce cyber security digital
media (as per Thomas’s thomas2020discursive  concept of a ‘discursive digital
archive’).777Statistics from the U.S. Bureau of Labor Statistics indicate that
18% of cyber security specialists are women jethwani2017can , which may
provide a reference point for the 37% of human images featuring feminine-
presenting people.

Our image-set also seems to attribute disparate expertise — and thus
responsibility for cyber security — to different individuals. The hackerman,
for instance, is presented as a ‘lone wolf’, whereas non-malicious individuals
are more frequently depicted in groups than as solo actors.888This is
particularly pronounced in images featuring feminine-presenting people, who in
many cases are accompanied by masculine-presenting people. One could interpret
this to mean that individuals lack the talents or other requisite knowledge
that the lone hackerman possesses hack , and must therefore work together to
counter the hackerman’s threats. The media promotes this association by
applying the hackerman stereotype to organisations such as ‘Anonymous’ and
online communities such as 4chan, placing these groups in a position to
dominate the conversation. In turn, individual users may view these entities
as malicious experts, abdicating their own responsibility for cyber security
based on their feelings of powerlessness hack .

Finally, our image-set features moral ambiguity and vague representations that
could make it difficult for users to derive context from images. For example,
while the hackerman is supposed to engage in malicious online activities, he
is sometimes presented with morally ambiguous or vigilante imagery like the ‘V
for Vendetta’ mask, complicating the viewer’s understanding of his aims. While
non-malicious individuals are consistently depicted as benign or neutral, they
are frequently engaged in a variety of nondescript tasks, like interacting
with hacked devices, responding to being hacked, or performing some kind of
professional work (for instance, as cyber security professionals). Images of
human anatomy were similar; though hands are an important and highly visible
part of the human body jakubietz2005defining  that can serve as a heuristic to
facilitate learning goldin2005our ,999Hands are often used to model movements
or convey information by assuming specific positions. in our image-set they
were engaged in a variety of mostly unclear / un-directed movements and
positions, often in connection with physical devices like laptops. Images in
which the eye was dominant were similarly varied, but in many cases
represented the moral ambiguity of a panopticon (alongside images from other
classes that render a retina and cornea from composite imagery) or enjoined
the user to pay attention gaines2019machinic .

By depriving users of role models, clear contexts / goals, and the means or
abilities to achieve such goals, these issues likely undermine individual
users’ self-efficacy. We believe that these issues stem, at least in part,
from cyber security’s reliance on stock photography. Stock photography is
characteristically nondescript and visually homogeneous because it must make
individual images salient to various use cases — hence the images of
individuals in vague contexts and unclear narratives. Furthermore, stock
photography in western media features a ‘discrimination implied by a well-
calculated, almost mandatory inclusion of gender and ethnic minorities’
papadopoulou2014seen , yielding the nominally inclusive images that
nonetheless fail to actually bestow agency to the individuals represented.

###  5.4 Digital-Physical spheres

Representing 7% of our total image-set, hybrid digital-physical
representations, such as digital networks superimposed over cityscapes, the
earth, or a more abstract sphere, were of some note to us. To understand what
these images mean and how they may affect self-efficacy, we consulted work in
other domains that use similar styles of visualisation. Most prominently,
Sloterdijk, a German philosopher, studied the history of spherical maps,
overlays, and designs, tracing these visual tactics as far back as the late
15th Century sloterdijk2011spheres . Sloterdijk argues that these kinds of
images arose naturally from our changing understanding of our planet at that
time, which was no longer an enclosed space or the centre of the universe, but
a single, contingent celestial body. According to this account, disoriented
European map makers began to fetishise spherical imagery as a sense-making
device capable of conveying that we were no longer living inside a world, but
rather on one.

Although the overlays in our image-set differ from these earlier precedents in
that they overlay networked security graphs instead of shipping lanes, they
may nonetheless embody a return to such ‘spheric-security’ in the face of new
metaphysical uncertainties, at least from an aesthetic viewpoint
sloterdijk2011spheres . Indeed, a subconscious spheric-security can also be
seen in images outside our image-set like network traffic maps, which often
take a spherical view despite not being technically constrained in this
manner. According to 10.1093/cybsec/tyv004 , spherical shapes suggest
something which can be contained and kept secure, and so they may help to
imaginatively ‘bound’ the sprawling endlessness of Network traffic, which
always threatens to deviate into the unknown.

###  5.5 Trends and Recommendations

In the above, we explored the attributes of cyber security aesthetics and
speculated about the kind of self-efficacy that these attributes afford. In
the following, we highlight the trends we observed across classes and the key
issues that cyber security aesthetics must address to improve its effect on
users’ self-efficacy.

####  5.5.1 A practical philosophy for cyber security

According to the philosophical discourse on aesthetics presented in Section 2,
aesthetics provide a basis for savoring sensation, organising sensations into
orderly meaning Kant1892-KANTCO-3 , and orienting contexts
kikuchi1992philosophic . Moreover, the meanings and contexts thus constructed
can influence our decisions chatterjee2014aesthetic . While cyber security
aesthetics seem to fulfil the first element of sensation, adapting a long
history of mathematical and even science fiction aesthetics (alongside other
disciplinary symbols) to frame cyberspace as an other-worldly future domain
that is not restricted to what we currently understand, they fail to provide
sufficient context for effective navigation or learning. Instead, they present
a dazzling spectacle of abstractions and powerful traditional security
semiotics without much in the way of meaning. The hackerman archetype,
powerful but elusive, and our other subjects involved in procedural and
ambiguous work, encourage users to abdicate responsibility based on their own
comparative lack of expertise and perceived powerlessness, while the digital-
physical spheres, like Sloterdijk’s historical spheres, reify a world that may
not match our experience of reality. According to Sloterdijk, humans no longer
believe in an all-seeing singularity encompassing us, be it supernatural or
human exceptionalism sloterdijk2011spheres . Nonetheless, much user-facing
cyber security media depicts cyberspace as a fraught and hostile environment
that individuals can’t hope to navigate without expert assistance, a strategy
that amounts to ‘fear-mongering’ and allows those with vested interests in the
cyber security field to turn security into an all-important and all-
encompassing issue neocleous2008critique .

Solving these problems is no simple task, and so no easy solution presents
itself. One high-level, long-term suggestion is to develop a practical
philosophy based on narratives. For example, mcsweeney1999security  frame
security as a form of resilience that individuals can build through everyday
tasks that make them feel secure. Where the person performing these tasks is a
trusted friend or confidante, security could also be framed as relational
security. Breaking cyber security down into small, actionable tasks can
significantly improve users’ self-efficacy, given the theory of self-efficacy
presented in Section 2. It would also allow cyber security aesthetics to play
a positive role, enabling individuals and society at large to navigate the
rapidly changing digital-physical world represented in our image-set. This
possibility could be realised through new and innovative semiotics that are
not simply copies of the traditional security landscape, or through a more
positivist and standardised form of low-abstracted relational imagery with
clear links to cyberspace. A practical example of this can be found in refuse
recycling, where Gary Anderson, a student, won a nationwide contest for a new
symbol for the then fledgling recycling initiative with his Mobius loop-based
three-chasing-arrows. This symbol has since risen to global prominence
jones1999gary .

####  5.5.2 Improving role models for cyber security

Systemic under-representation in certain occupations is a complex and multi-
causal problem that needs to be examined using both interdisciplinary and
context-specific approaches.101010These approaches must also account for
under-representation at multiple phases / points in time, including factors
that influence the admission, participation, and progression of under-
represented individuals in these industries. Fortunately, we can begin to
bolster all users’ self-efficacy through much more straightforward
steps.111111In our case, self-efficacy is linked to the context in which these
images are used for informally learnt cyber security. For example, insofar as
stereotypes can have positive self-efficacy effects in certain contexts
czopp2015positive ,121212In the field of education, positive stereotypes have
been shown to influence which goals individuals choose to pursue
czopp2015positive . we could co-opt the male-dominated hackerman stereotype
and make it more inclusive, extending its connotations of moral ambiguity and
power to individuals across demographics. We believe that such role models can
help to reduce users’ cognitive load when assimilating cyber security
knowledge, and that they can make the field as a whole seem more user-
friendly.

####  5.5.3 The paradox of simplification

In the world of user interface design, developers build persuasive and easy-
to-use interfaces to pursue a kind of universal simplicity. This objective
could also be applied to cyber security aesthetics, where it could help to
magnify users’ self-efficacy. For instance, cyber security aesthetics could
become simpler, cleaner, and more pertinent to the subject matter, or they
could feature more rhetorical imagery. Where the digital workspaces, networked
landscapes, text walls, and semiotic padlocks in our image-set all pose
exploratory questions (prompting internal narrative reflection), rhetorical
imagery makes a specific point, is designed with a specific audience in mind,
and is focused on narrative integrity above all.

Of course, the problem is not just about exploratory imagery; we have seen
that cyber security aesthetics feature many abstractions and visual devices
that confuse the core concept being depicted. If cyber security aesthetics are
supposed to simplify the complex nature of cyber security, it appears that we
must first simplify the aesthetics themselves. However, simplification can
itself lead to misunderstanding, as was the case in the Space Shuttle
disaster, which was partially attributable to the oversimplification of data
in a graph tufte2006beautiful . This is the cyber security aesthetic paradox:
that simplification can aid as well as hinder understanding in equal measure.
To overcome this challenge, we might look to other fields that have developed
unique aesthetic norms that enhance learners’ self-efficacy. The field of
chemistry, for instance, has spent centuries developing a standard aesthetic
system that simplifies complex concepts and narratives without rendering them
ineffective hoffmann2003thoughts .

##  6 Limitations and future research directions

The scope of this study was limited by the definitions we used and the
selection criteria we applied to guide our assembly of the image-set. In our
case, this meant focusing on English-language material even though a
preliminary search conducted before implementation unearthed a rich catalogue
of images in other languages. This also means that our analysis and our
findings likely exhibit Anglo-Saxon bias. Nevertheless, we expect that our
methodology can be adapted to explore the same research aims in other
languages and cultural contexts, enabling a more universal understanding of
cyber security aesthetics.

Given the background we provided in Section 2, our definition of aesthetics
likely exhibits Anglo-Saxon or broadly Euro-American bias, failing to
encapsulate the aesthetic philosophies of other cultures. It may also be
limiting for other reasons, as we focused narrowly on their ability to provide
context for, and inlays to, the world of cyberspace, making it more
interpretable and navigable for informal learners. Other definitions might
yield different insights and support other kinds of research questions.

We utilised semi-automated methodologies to classify images based on the
semiotic objects within them, and the results are tempered by the respective
limitations of these methodologies. Moreover, our results represent a specific
snapshot in the security timeline; access to a larger historical image-set
would inevitably change the overall results, potentially yielding a more
statistically significant sentiment analysis. Furthermore, in order to
assemble a unique image-set, we ignored duplicates. However, insofar as
aesthetic literacies arise from exposure to images, rather than the absolute
number of unique images, including duplicates could help us to ascertain the
effective rate of gender representation, as perceived by viewers.

This study was exploratory, and we predicted how users’ self-efficacy might be
affected by images through a singular focus on qualitative analysis. We did
not consider other metrics that could have enhanced the findings, and we did
not engage in other kinds of data collection (like surveys) that could have
revealed real users’ reactions. Traditionally speaking, image recognition
assessments in lab settings involve comprehension tests, eye tracking, and
brain-imaging. Knowledge of how users interact with cyber security aesthetics
in these terms would allow for a significantly richer analysis of aesthetic
effects on self-efficacy.

Finally, because self-efficacy is a fluid construct that may vary based on
specific emotional awareness and specific tasks or contexts
karademas2007optimism , future research in this field could assess the
emotional associations of more granular aesthetic elements, like each colour
within each image class. For example, mohammad2013colourful  crowd-sourced an
inventory of colour-word associations that reveal the specific emotions
attached to specific colours, which could be used to analyse the colour trends
in our image-set.

All of these limitations present myriad opportunities to expand on this work,
perfecting our understanding of cyber security aesthetics and the effects it
can have on cyberspace and its users.

##  7 Conclusion

In this paper we have presented work on an image-set of cyber security
aesthetics generated from mainstream media articles as might be faced by
individual users on a regular basis. The work was oriented by two aims: (1) to
ascertain what cyber security aesthetics consist of, from the perspective of
an individual user, and (2) to provide an explorative discussion as to the
manner in which these aesthetics may affect users’ perceived self-efficacy as
they informally learn cyber security precepts.

Our findings for (1) indicate that cyber security aesthetics depict a
threatening and confusing environment with systemic semiotic and social
deficiencies — a distorted vision of cyberspace without clarity of thought.
The narrative of cyber security is abstract and opaque, but through informal
learning, individual users can assemble a mental representation of this
concept, using their senses to intuit meaning and perspective from the visual
elements that accompany cyber security texts.

For (2) our findings raise important obstacles to self-efficacy potential,
from the way that participants of cyberspace are portrayed to the moral
ambiguity that characterises a significant proportion of the image-set.
Nonetheless, as cyber security continues to evolve into a core concept within
cyberspace, we believe that these issues can be overcome. Indeed, several of
these problems, and the simplification paradox itself, arise from a lack of
vision, or perhaps just the lack of usable semiotics available to cyber
security content creators. We believe the work represents the first steps in
working towards a more holistic and cohesive cyber security aesthetic vision.

## Statements and Declarations

All authors contributed to the study conception and design. Material
preparation and data collection were performed by [Mark Quinlan]. Initial data
analysis of the image-set were performed by [Mark Quinlan] and [Aaron Ceross],
with all further analysis performed by [Mark Quinlan]. The first draft of the
manuscript was written by [Mark Quinlan] and all authors commented on previous
versions of the manuscript. All authors read and approved the final
manuscript.

The authors declare that no funds, grants, or other support were received
during the preparation of this manuscript, and have no relevant financial or
non-financial interests to disclose.

## References

  *   * (1) Lin, F., Yao, M.: The impact of accompanying text on visual processing and hedonic evaluation of art. Empirical Studies of the Arts 36(2), 180–198 (2018)
  * (2) Hattwig, D., Bussert, K., Medaille, A., Burgess, J.: Visual literacy standards in higher education: New opportunities for libraries and student learning. portal: Libraries and the Academy 13(1), 61–89 (2013)
  * (3) Nichols, J.: Illustrations of the Literary History of the Eighteenth Century vol. 8. Nichols; Bentley, ??? (1858)
  * (4) Marland, A.: Political photography, journalism, and framing in the digital age: The management of visual media by the prime minister of canada. The International Journal of Press/Politics 17(2), 214–233 (2012)
  * (5) Cooper, N., Holman, V.: War and visual culture since 1900. Journal of War & Culture Studies 1(3), 219–222 (2008)
  * (6) Meyer, B., Verrips, J.: Aesthetics. Routledge, ??? (2008)
  * (7) David, A., Glore, P.: The impact of design and aesthetics on usability, credibility, and learning in an online environment. Online Journal of Distance Learning Administration 13(4) (2010)
  * (8) Mitchell, C., Weber, S., O’Reilly-Scanlon, K.: Just who do we think we are. Methodologies for (2005)
  * (9) Page, T.: Skeuomorphism or flat design: future directions in mobile device user interface (ui) design education. International Journal of Mobile Learning and Organisation 8(2), 130–142 (2014)
  * (10) Curtis, A.: Rhetoric of Flat Design and Skeuomorphism in Apple’s iOS Graphical User Interface. University of Rhode Island, ??? (2015)
  * (11) Tharangie, K., Irfan, C., Marasinghe, C., Yamada, K.: Kansei engineering assessing system to enhance the usability in e-learning web interfaces: Colour basis. In: 16th International Conference on Computers in Education, vol. 1, pp. 145–150 (2008)
  * (12) Reyna, J.: The importance of visual design and aesthetics in e-learning. Training and Development 40(5), 28–31 (2013)
  * (13) Malcolm, J., Hodkinson, P., Colley, H.: The interrelationships between informal and formal learning. Journal of Workplace Learning (2003). https://doi.org/10.1108/13665620310504783
  * (14) Ollis, T.: Learning in social action: The informal and social learning dimensions of circumstantial and lifelong activists. Australian Journal of Adult Learning 51(2), 248–268 (2011)
  * (15) Flavián, C., Gurrea, R., Orús, C.: A heuristic evaluation of websites design for achieving the web success. International Journal of Services and Standards 5(1), 17–41 (2009)
  * (16) Joshi, D., Datta, R., Fedorovskaya, E., Luong, Q.-T., Wang, J.Z., Li, J., Luo, J.: Aesthetics and emotions in images. IEEE Signal Processing Magazine 28(5), 94–115 (2011)
  * (17) Shires, J.: Cyber-noir: Cybersecurity and popular culture. Contemporary Security Policy 41(1), 82–107 (2020)
  * (18) Carroll, F.: Usable security and aesthetics: Designing for engaging online security warnings and cautions to optimise user security whilst affording ease of use. In: European Symposium on Usable Security 2021, pp. 23–28 (2021)
  * (19) Fogg, B.J.: A behavior model for persuasive design. In: Proceedings of the 4th International Conference on Persuasive Technology, pp. 1–7 (2009)
  * (20) Robinson, L., Schulz, J., Blank, G., Ragnedda, M., Ono, H., Hogan, B., Mesch, G., Cotten, S.R., Kretchmer, S.B., Hale, T.M., et al.: Digital inequalities 2.0: Legacy inequalities in the information age (2020)
  * (21) Alsudani, F., Casey, M.: The effect of aesthetics on web credibility. People and Computers XXIII Celebrating People and Technology, 512–519 (2009)
  * (22) Ma, K.-L.: Cyber security through visualization. In: Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation-Volume 60, pp. 3–7 (2006). Citeseer
  * (23) Bernal, V.: 1\. the aesthetics of cyber insecurity: Displaying the digital in three american museum exhibits. In: Futureproof, pp. 33–62. Duke University Press, ??? (2020)
  * (24) Ghertner, D.A., McFann, H., Goldstein, D.M.: Futureproof: Security Aesthetics and the Management of Life. Duke University Press, ??? (2020)
  * (25) Dewey, J.: Art as experience. New York: wideview. Pedigree Books (1934)
  * (26) Beardsley, M.C.: Aesthetics from Classical Greece to the Present vol. 13. University of Alabama Press, ??? (1975)
  * (27) Kant, I.: The Critique of Judgment. Prometheus Books, ??? (1892)
  * (28) Zander, T., Öllinger, M., Volz, K.G.: Intuition and insight: Two processes that build on each other or fundamentally differ? Frontiers in Psychology 7, 1395 (2016)
  * (29) Walsh, D.: Aesthetic objects and works of art. The Journal of Aesthetics and Art Criticism 33(1), 7–12 (1974)
  * (30) Wissenburg, I.: Aesthetic communication on the possibility of combining semiotics and aesthetics: analysis of ‘delftsche slaolie’by jan toorop. B.S. thesis, Universiteit Utrecht (2012)
  * (31) Barbosa, S.D.J., Barbosa, G.D.J., Souza, C.S.d., Leitão, C.F.: A semiotics-based epistemic tool to reason about ethical issues in digital technology design and development. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 363–374 (2021)
  * (32) Cheng, L., Pei, J., Danesi, M.: A sociosemiotic interpretation of cybersecurity in u.s. legislative discourse. Social Semiotics 29(3), 286–302 (2019) https://doi.org/10.1080/10350330.2019.1587843. https://doi.org/10.1080/10350330.2019.1587843
  * (33) Sayers, S.: Jacques rancière (2004) the politics of aesthetics: the distribution of the sensible. Culture Machine, Reviews (2005)
  * (34) Rudner, R.: On semiotic aesthetics. The Journal of Aesthetics and Art Criticism 10(1), 67–77 (1951)
  * (35) Chatterjee, A.: The Aesthetic Brain: How We Evolved to Desire Beauty and Enjoy Art. Oxford University Press, ??? (2014)
  * (36) Carper, B.A.: Fundamental Patterns of Knowing in Nursing. Teachers College, Columbia University, ??? (1975)
  * (37) Keenan, T.M.: The use of aesthetic knowledge in decision making processes in mega projects. PhD thesis, Queensland University of Technology (2016)
  * (38) Carroll, F.: Designing (for) experiences in photorealistic vr environments. New Review of Hypermedia and Multimedia 16(1-2), 181–194 (2010)
  * (39) Fernandes, M., Walls, L., Munson, S., Hullman, J., Kay, M.: Uncertainty displays using quantile dotplots or cdfs improve transit decision-making. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2018)
  * (40) Rader, E., Wash, R., Brooks, B.: Stories as informal lessons about security. In: Proceedings of the Eighth Symposium on Usable Privacy and Security, pp. 1–17 (2012)
  * (41) Bandura, A., Freeman, W.H., Lightsey, R.: Self-efficacy: The Exercise of Control. Springer, ??? (1999)
  * (42) Maddux, J.E.: Self-efficacy theory. In: Self-efficacy, Adaptation, and Adjustment, pp. 3–33. Springer, ??? (1995)
  * (43) Stumpf, S.A., Brief, A.P., Hartman, K.: Self-efficacy expectations and coping with career-related events. Journal of Vocational Behavior 31(1), 91–108 (1987)
  * (44) Herley, C.E.: So long, and no thanks for the externalities: The rational rejection of security advice by users. In: Proceedings of the 2009 Workshop on New Security Paradigms Workshop. NSPW ’09, pp. 133–144. Association for Computing Machinery, New York, NY, USA (2009). https://doi.org/10.1145/1719030.1719050. https://doi.org/10.1145/1719030.1719050
  * (45) Halevi, T., Memon, N., Lewis, J., Kumaraguru, P., Arora, S., Dagar, N., Aloul, F., Chen, J.: Cultural and psychological factors in cyber-security. In: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services, pp. 318–324 (2016)
  * (46) Ajzen, I.: The theory of planned behaviour. organizational behaviour and human decision processes, 50 (2), 179-211. View at (1991)
  * (47) Bosma, N., Hessels, J., Schutjens, V., Van Praag, M., Verheul, I.: Entrepreneurship and role models. Journal of economic psychology 33(2), 410–424 (2012)
  * (48) Marres, N., Weltevrede, E.: Scraping the social? issues in live social research. Journal of Cultural Economy 6(3), 313–335 (2013). https://doi.org/10.1080/17530350.2013.772070
  * (49) Schatz, D., Bashroush, R., Wall, J.: Towards a more representative definition of cyber-security. Journal of Digital Forensics, Security and Law 12(2), 53–74 (2017). https://doi.org/10.15394/jdfsl.2017.1476
  * (50) Humayun, M., Niazi, M., Jhanjhi, N., Alshayeb, M., Mahmood, S.: Cyber-security threats and vulnerabilities: a systematic mapping study. Arabian Journal for Science and Engineering 45(4), 3171–3189 (2020)
  * (51) Kosar, T., Bohra, S., Mernik, M.: Protocol of a systematic mapping study for domain-specific languages. Journal of Information and Software Technology 21(C), 77–91 (2016)
  * (52) Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, vol. 1, pp. 1–2 (2004). Prague
  * (53) Weller, H.I., Westneat, M.W.: Quantitative color profiling of digital images with earth mover’s distance using the R package colordistance. PeerJ 7, 6398 (2019)
  * (54) Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)
  * (55) Gupta, A., Thakkar, K., Gandhi, V., Narayanan, P.: Nose, eyes and ears: Head pose estimation by locating facial keypoints. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1977–1981 (2019). IEEE
  * (56) Wäldchen, J., Mäder, P.: Plant species identification using computer vision techniques: A systematic literature review. Archives of Computational Methods in Engineering 25(2), 507–543 (2018)
  * (57) Samek, W., Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R.: Evaluating the visualization of what a deep neural network has learned. IEEE transactions on neural networks and learning systems 28(11), 2660–2673 (2016)
  * (58) Johnson, M.R.: The history of cyberspace aesthetics in video games. In: Cyberpunk and Visual Culture, pp. 139–154. Routledge, ??? (2017)
  * (59) Gibson, W.: Neuromancer vol. 1. Aleph, ??? (1984)
  * (60) Benedikt, M.L.: Cyberspace: First Steps. MIT Press, ??? (1991)
  * (61) Branch, J.: What’s in a name? metaphors and cybersecurity. International Organization 75(1), 39–70 (2021). https://doi.org/10.1017/S002081832000051X
  * (62) Nye, D.E.: American Technological Sublime. mit Press, ??? (1996)
  * (63) Featherstone, M., Burrows, R.: Cyberspace/cyberbodies/cyberpunk: Cultures of Technological Embodiment. Sage, ??? (1996)
  * (64) Croon, A.: Making sense of cyberspace: a question of being-with information technology. In: Exploring Cyber Society, pp. 1–9 (1999). Univeristy of Northumbria at Newcastle
  * (65) Shedroff, N., Noessel, C.: Make It So: Interaction Design Lessons from Science Fiction. Rosenfeld Media, ??? (2012)
  * (66) Greenspan, S.: Future screens are mostly blue (2013). https://99percentinvisible.org/episode/future-screens-are-mostly-blue/
  * (67) Beasley, R., Danesi, M.: Persuasive Signs: The Semiotics of Advertising vol. 4. Walter de Gruyter, ??? (2010)
  * (68) Kearney, R., Rasmussen, D.: Continental aesthetics: Romanticism to postmodernism: An anthology (2001)
  * (69) Thomas, P.: Discursive trick effects: How raced and gendered semiotics in industry media undermine equal representation in the cybersecurity workforce (2020)
  * (70) Jethwani, M.M., Memon, N., Seo, W., Richer, A.: “i can actually be a super sleuth” promising practices for engaging adolescent girls in cybersecurity education. Journal of Educational Computing Research 55(1), 3–25 (2017)
  * (71) Sob, T.: “hackerman”: How diverging cyberspace portrayals influence the adf’s perception of cybersecurity and the cultural ramifications of these judgements. Australian Defense Force - Cyber Security Challenges 1(1) (2021)
  * (72) Jakubietz, R.G., Jakubietz, M.G., Kloss, D., Gruenert, J.G.: Defining the basic aesthetics of the hand. Aesthetic plastic surgery 29(6), 546–551 (2005)
  * (73) Goldin-Meadow, S., Wagner, S.M.: How our hands help us learn. Trends in cognitive sciences 9(5), 234–241 (2005)
  * (74) Gaines, B.: Machinic eyes: New and post-digital aesthetics, surveillance, and resistance. PhD thesis, Clemson University (2019)
  * (75) Papadopoulou, A.: As seen in your prospectus: A critical essay on the representation of ethnic diversity in stock photography (2014)
  * (76) Sloterdijk, P.: Spheres volume i: Bubbles microspherology. Los Angeles: Semiotext (e) (2011)
  * (77) Hall, P., Heath, C., Coles-Kemp, L.: Critical visualization: a case for rethinking how we visualize risk and security. Journal of Cybersecurity 1(1), 93–108 (2015) https://academic.oup.com/cybersecurity/article-pdf/1/1/93/7001223/tyv004.pdf. https://doi.org/10.1093/cybsec/tyv004
  * (78) Kikuchi, J.F., Simmons, H.: Philosophic inquiry in nursing (1992)
  * (79) Neocleous, M.: Critique of Security. Edinburgh University Press, ??? (2008)
  * (80) McSweeney, W., Bill, M.: Security, Identity and Interests: a Sociology of International Relations vol. 69. Cambridge University Press, Cambridge, ??? (1999)
  * (81) Jones, P., Powell, J.: Gary anderson has been found! Resource Recycling 18, 25–27 (1999)
  * (82) Czopp, A.M., Kay, A.C., Cheryan, S.: Positive stereotypes are pervasive and powerful. Perspectives on Psychological Science 10(4), 451–463 (2015)
  * (83) Tufte, E.R.: Beautiful Evidence. Graphis Pr, ??? (2006)
  * (84) Hoffmann, R.: Thoughts on aesthetics and visualization in chemistry. HYLE–International Journal for Philosophy of Chemistry 9(1), 7–10 (2003)
  * (85) Karademas, E.C., Kafetsios, K., Sideridis, G.D.: Optimism, self-efficacy and information processing of threat-and well-being-related stimuli. Stress and Health: Journal of the International Society for the Investigation of Stress 23(5), 285–294 (2007)
  * (86) Mohammad, S.: Colourful language: Measuring word-colour associations. arXiv preprint arXiv:1309.5942 (2013)