ArticlePDF Available

Visualizing Dilemmas: when information and biology converge in the visualization of viruses

Authors:

Abstract and Figures

Computer and biological viruses: principles of cohesion. Technically speaking, computer and biological viruses are affiliated to two unbridgeable and well-separated spheres, one prevalently pertaining to the domain of information and the other to the one of carbon-based life. Their material formation contributes to such divergence: while computer viruses are normally fabricated by and partially depending on human agency, biological viruses are mostly understood as naturally occurring. Worms, Trojan horses and computer malware are often described as if they were digital version of the natural ecosystem. However, a real intertwining and merging with such system is still confined to the domain of science fiction. 1 The two realms do not speak to each other. While the first only understands the lingo proper of information technologies, the second takes in the language of the biological. Communicating by means of binary code, computer viruses are a separate category. Their circulation within the world of digital circuits, and electronic networks, makes them unquestionably distinct from their biological counterparts. There seems to be no real intersection between the two categories. Despite the factual and easily discernible discrepancies between computer and biological viruses, the first are affiliated with the latter, to the extent that, in most cases, their existences appear intimately entangled. One element seems to confirm this kinship: their invisibility. With their submicroscopic size, biological viruses constitute some of the smallest biological agents known. This makes them inscrutable to the human eye.
Content may be subject to copyright.
1
Visualizing Dilemmas: when information and biology converge
in the visualization of viruses.
Roberta Buiani
Computer and biological viruses: principles of cohesion.
Technically speaking, computer and biological viruses are affiliated to two
unbridgeable and well-separated spheres, one prevalently pertaining to the domain of
information and the other to the one of carbon-based life. Their material formation
contributes to such divergence: while computer viruses are normally fabricated by and
partially depending on human agency, biological viruses are mostly understood as
naturally occurring. Worms, Trojan horses and computer malware are often described as
if they were digital version of the natural ecosystem. However, a real intertwining and
merging with such system is still confined to the domain of science fiction.1The two
realms do not speak to each other. While the first only understands the lingo proper of
information technologies, the second takes in the language of the biological.
Communicating by means of binary code, computer viruses are a separate category. Their
circulation within the world of digital circuits, and electronic networks, makes them
unquestionably distinct from their biological counterparts. There seems to be no real
intersection between the two categories.
Despite the factual and easily discernible discrepancies between computer and
biological viruses, the first are affiliated with the latter, to the extent that, in most cases,
their existences appear intimately entangled. One element seems to confirm this kinship:
their invisibility. With their submicroscopic size, biological viruses constitute some of the
smallest biological agents known. This makes them inscrutable to the human eye.
2
Strolling the Internet and hiding in the most recondite folds of our computers, computer
viruses are mainly made of code. The user needs a considerable amount of technical
skills to detect them. Once disassembled, they provide nothing to the user's sight.
Western culture is dominated by the philosophical principle that “seeing” is
necessary to “knowing.” “The scientific notions of ‘objectivity (Van Loon 2005, 112),’"
Van Loon observes, "has always (or at least since Plato) been ordained by the principle of
visibility." If, in order to be credible and properly analyzed, the object must be observable
(109), then, viruses remain unknown and pervaded by an overall sense of indeterminacy.
The lack of visibility, as instance of the unknown, is a considerable source of fear and
anxiety. In the case of viruses, this factor adds to their already infamous popularity as
mainly dangerous (or evil) agents.
This invisibility is not just source of fear. At the opposite end of the spectrum, it
nurtures a desire to overcome the indeterminacy that pervades viruses, generating a
continuous drive to further explore their behavior and composition. As reaction to the
anxiety produced by viruses or, on the contrary, as the product of a drive to domesticate
them, stand many attempts to describe and represent these agents, and to create tools to
manage such invisibility.
Using a number of technological devices, from invisible, formless substances,
viruses can be turned into visible objects. Knowledge acquired through this
objectification has a double function: it leads not only to understand but also to
eventually neutralize the anxiety engendered by these substances. This has implications
in the display of the final picture. In fact, technologies intervene to clarify, specify and
objectify. At the same time, they act as a protective layer between the scientist and the
3
"object", the user and the code. Various devices (electron microscopy, customized data
retrieval software or 3D modeling) are juxtaposed to assign an appearance to viruses by
associating the energetic signals they emit or the numbers and algorithms they are made
of, to a specific color, shape, behavior. Paradoxically, the very technologies that should
reveal, by displaying viruses (thus contributing to eliminate the fear they cause) end up
making them disappear under multiple layers of technology and interpretations. A
product of this trend, visualizations of computer and biological viruses reach a stage in
which they increasingly approximate each other.
From a safe distance.
Visual representation challenges the domain of the unknown. At the same time, it
protects from the obscure domain of the unknown. Pictures, maps and models constitute
different ways of making the invisible visible, to order the indeterminacy of chaos and to
“neutralize and contain what provoked anxiety and distress (Bauman 2005, 20)” in the
first place.
Companies and research units involved in the field of visualization describe their
own activity as “the final frontier (In-Vsee 2008; Coy 1996)2.” According to this notion,
Virilio illustrates, the unknown is a continuously shifting horizon to be conquered and
turned into the known. To do this, risky explorations have been undertaken to push the
border of the unknown further away, and increasingly sophisticated technologies
(microscopes and telescopes) have been devised to conquer the infinitely small and the
extremely far away (Virilio 2000, 54). No doubt, viruses represent a challenge to explore,
or to move the boundaries that delimit such frontier. Using better tools able to ‘see’
viruses, to enhance the understanding of these submicroscopic and ephemeral agents, it is
4
implied, would ultimately liberate us from the looming anxieties they are able to
generate.
The use of the "myth of the frontier" and the notion of knowing as “conquering
the unknown” are equally reflected in the scientific and in the computer fields. In both
cases, viruses have to be known before they are domesticated or neutralized. For malware
analyst and technical educator Randy Abrams, although
..The common reaction to the unknown is fear and panic….
Understanding brings appropriate reactions
Understanding allows for appropriate preventive actions
Understanding may decrease fear (Abrams, 2006)
Thus, conquering the mystery of viruses is not the same as simply gathering information.
It unfolds as a defensive gesture, as if knowing meant to single-out and to de-limit the
object of inquiry within a visual and material space, with the purpose of minimizing its
damaging potential and taking distance from it.
With his notion of knowing as removing the horizon that separates the known
from the unknown, Virilio confirms the centrality of sight as the major sense we rely on
to put a face to the unknown or the undefined. Card, Mckinlay and Schneiderman agree:
…To understand something is called ‘seeing it.’ We try to make our ideas ‘clear’,
to bring them into ‘focus’ to ‘arrange’ our thoughts. The ubiquity of visual
metaphors in describing cognitive processes hints at a nexus of relationship
between what we see and what we think (Card, Mckinlay and Schneiderman
1999, 1).
While seeing means to uncover, to explore and to understand the unknown, it is
also a strategy to neutralize it. The inability to see is disorienting. “Peur toujours, peur
partout (Bauman, 2005, 2).” With these words, Bauman recalls Febvre’s description of
“the experience of living in the XVI century Europe,” when ubiquitous fear was evoked
5
by darkness, “..which started just on the other side of the hut door and wrapped the world
beyond the farm fence…(2).” Darkness does not necessarily stand for any threat.
However, it is the natural locus of uncertainty and mystery, or the place where anything
could happen (4).
“Images and image technologies are involved in the way we know, experience,
feel about and respond to the world”(5). In a sense, there is an affinity between modern
utopias and the new frontier: “insofar as real spaces elsewhere have been exhausted, it is
necessary to find new kinds of place and frontier that sustain the needs of the modern
imaginary (15).” Whether through the implementation of technologies or through plain
research, with its current richness, and productivity, the industry involved in assigning a
visual appearance to viruses embraces the above notion of knowledge.
Viewing, visualizing and materializing the unknown summarizes both the
curiosity in imagining it as well as the anxiety it engenders (Robins 1999, 58). Seeing
becomes an act of re-presenting as well as a defense strategy against the demon of the
unknown. Visualization methods and technologies are called upon as a protective screen
that transcends and separates humankind from the fearful chaos of the world, by making
every object visible, yet maintaining it substantially virtual and at a safe distance. While
every picture produced is a way to unveil yet another facet of viruses, it incorporates the
anxiety and the fear of the unknown that has characterized the drive to portray and
picture viruses in the first place.
Commenting on the visual materialization extracted from data and specific
information, McCormick et al. (1987) observe how these images "transform the symbolic
into the geometric." In fact, the challenge of information visualization, Chen confirms, is
6
to “come up with a design metaphor that will accommodate the transformations from
non-spatial, non-numerical concepts to something visible and meaningful” and will make
sure that the “particular metaphor does, indeed, work (Chen 2005, 29)." Thus, visual
representations constitute a rationalization of the object. A way to unravel the unknown,
visualization isolates something immaterial, it extracts its crucial information, it
processes such information and turns it into a concrete—almost tangible—object.
Technology-mediated vision, we are reminded, excludes the contact with its
reality (Robins 1999). This attitude is neither unlike the one proposed by modernist
planners and architects, nor is it radically different from the objectives that lie behind
today’s ubiquitous surveillance cameras. Modernist architecture “sought to erase what we
may call the city of touch, and in its place to construct the glass city (19).” Its
fundamental objective was “that of transparency,” aspiring to the idea of universal
panopticism, through which it seems possible to “achieve order, and consequently master,
the urban space (20)." Like the glass window separating the observer from the observed,
thanks to the process of visualization, viruses are never experienced directly, but through
a number of filters and layered operations. Thus, they are separated from their observers.
On the one hand, these filters increasingly adjust the focus and define the contours of the
virus as object. On the other hand, they mislead and increasingly distance the viewer
from the object.
“In-vitro” and “forensic” approaches.
Both technologies and methods employed support a notion of knowledge that
privileges the act of “seeing from a safe distance” suggested by Robins. The tendency to
separate the observer from the observed is proper of the “in vitro” and "forensic"
7
approach." To be turned into a self-contained object, viruses are displaced from their
original location, frozen into a particular position. The substance is moved into a
controlled environment, such as the lab, or the test tube, and is turned into a self-
contained object. This allows the study of the object of inquiry from a safe distance
(Latour 1983; Stafford 2002). A forensic analysis intervenes to reconstruct and simulate
the behavior, and the damage caused by viruses, by inspecting and comparing the
condition of the host in relation to the virus before and after the infection has occurred.
While both approaches delineate and define the substance in its general features, they
also submit it to a process of transformation that ends up distancing the so-called object
from its previous embodied state.
Through different methods and technological instruments, disciplines that study
both biological and computer viruses appear to embrace identical approaches. The
practice of separating viruses from their initial milieu3 is common in microbiology and
virology, when the material that contains the viral substance is transferred from its
originally observed location into the laboratory, and it is analyzed inside the Petri dish (to
observe its growth) or on a slide. The methodological processes gradually transform the
virus. For instance, although the virus’ survival depends on the host and on the
surroundings' conditions, its visual representation portrays it as an isolated entity, or as a
virion. In this visual form, this object stands for the virus, although it does only epitomize
one of its facets.
A similar in-vitro method characterizes the study of computer viruses. Despite
becoming active only when they are launched inside an existing computer environment
(the host computer), and when they are triggered by an external event (such as the
8
opening of an attachment), no visual representation of viruses shows their relationship
with, and even their crucial dependence on such events. Even when sophisticated
animations are used to show how malware operates and moves to attach itself to the host
computer, they usually portray one single virus unfolding against a black background, as
if it was surrounded by a void environment, or the virus were located on a slide ready to
be scrutinized through a virtual microscope4.
A recent example of the in-vitro approach can be found in the use of honeypot
computing. Honeypots are computers or internal networks built to look like any computer
system. However, they are specifically devoted to capturing and trapping malware. This
strategy, claim computer analysts and researchers, is used to allow the study of malware
and to expedite the dissemination of new anti-virus solutions (Galloway & Thacker 2007,
104; Kaspersky 2006). The idea behind the honeypot is to create a safe environment for
experimentation, analogous to the safe environment of the lab. Once trapped inside,
viruses have no way to expand and infect other computers. Inside this virtual
environment, the computer virus can be analyzed and simulations (visual or not) of its
transformation and spread devised.
While the above method facilitates ad hoc studies to determine the effect of a
specific virus upon a virtual environment, it also, and inevitably, separates it from the rest
of its environment, the World Wide Web. Visual representations of the behavioral
patterns of viruses are based on the above premises. Admittedly drawn from the popular
TV series, FSCSI (F-Secure Crime Scene Investigation), consists in simulation via
animated sequence of how viruses infect data on the hard drive.
By generating an animation based on the analysis of forensic data, this simulation
9
visually identifies the areas most damaged:
..[FSCSI] records data before and after the [viral] sample is run and then
compares the two for changes: the intermediate stage, that is, the process that has
led to the infection is, then, guessed, based on the evidence provided by the
previous two states (Sean, 2007, F-secure weblog, comment posted July 5).
Fig. 1.-2. Patrik Runald demonstrating the FSCSI tool in Sidney, July 2007
The object portrayed is basically un-representable by means of traditional tools.
Thus, reflecting the assumed scientific paradigms and notion of knowledge illustrated
above, the technologies employed in the examination of viruses have a tendency to go
unnoticed. Both in-vitro and forensic methods extract and focus specifically on the
object-virus. The process(es) leading to the objectification certainly contribute to the
specification and sharpening of the object.
Observing the resulting image or the animation, Trumbo asserts (2006), even the
most attentive audience will have difficulties to tell whether an image published on a
science magazine "faithfully" represents the object, or is it just a colorful or science
fictional rendition. In fact, technologies of visualization are perceived as “transparent,
neutral intermediaries between the scientist and nature (Pasveer, 2006, 43)."
“Mechanical” objectivity projected by the instrument substitutes “the machine for the
10
human sensory apparatus (42)." With their magnifying, and enhancing capabilities, the
microscope, the screen, and the software are believed to stand for and substitute the eye.
However, technological devices are anywhere transparent. They abstract the
object, filter it, prepare it for observation. By standing between the object examined and
the eye, they act like a filter that facilitates the vision by selecting and extracting
information emitted by the object of study. If Van Loon argues, technoscience is driven
by a desire for the “colonization of the unknown,” it can only do so by “creating another
remainder: this remainder is none other than an index, which defies visualization (Van
Loon 2002, 108)." The technologies used are responsible for confirming such reminder,
by simultaneously revealing, while objectifying the virus, and building a distance
between the object and the viewer, thus, gradually withdrawing from such object. By
continuously iterating the idea of knowledge as both boundary-crossing and boundary-
building, these technologies simultaneously reflect and reinforce the anxieties and the
curiosity engendered by viruses.
From the microscope to the computer screen: the transformation of a “C
virus.”
If one excepts bigger substances closer to parasites, a light microscope is not able
to accurately grasp most viruses. When the first virus (tobacco mosaic) was successfully
visualized in 1939, it was by using an electron microscope, an instrument able to show
the entity as an “icon.” Wagner explains how visualization using a electron microscope
works:5
…Despite the value of the EM’s (Electronic Microscope) high resolving power,
the energy needed to attain short wavelengths poses a problem. High-speed
(short-wavelength) electrons are quite penetrating, and most biological subjects
are transparent to them. Thus, in order to visualize viruses, they are generally
11
either stained or coated with a heavy metal such as platinum or osmium. …The
particles then are visualized by passing electrons through the specimen and
observing it on a fluorescent screen (Wagner 2006, 120).
By projecting a focused beam of electrons onto a specimen, the electron
microscope returns surface information about various features of the virus, such as its
morphology and molecular structure (Lynch 2005). The conception and production of an
iconic visualization implies that the final image, to be appropriately viewed, has gone
through some degree of manipulation. During this process, the virus is turned into a
“virtual object” that has already passed through a number of phases before reaching its
current visible status (the substance has been stained or coated in another substance, and
information about its shape has been generated as a reflection, thanks to the bouncing off
of electrons on the surface of the virus) (Wagner 2006).
The snapshots produced by the electron microscope are usually black and white.
It is not uncommon to find an identical image displayed in different colors. Coloring is an
additional technique applied to further specify and "bring up" further details in the
structure of a viral agent. Subsequent technological devices are, then, deployed to process
and select the initial data provided by the microscope. Following this procedure, basic
images of a common type C flu virus6 may be colored and focused to highlight a
particular detail or to distinguish the parts that compose the “body” of the substance.
Fig.3 C virus. b/w EM snapshot
Fig. 4 C virus. EM snapshot colored in green
12
Obtained by freezing the object at cryogenic temperatures, data can be retrieved
through EM electron microscopes and additionally processed with one or, more, custom
software that gather, separate by typology (molecular composition, genomics, structure)
and then, display, three dimensional imaging data of the molecular structure and other
properties of viruses. The resulting image reminds us of the previous snapshot. However,
the amount of details and information the picture is able to provide are countless.
Fig. 5 C virus. Structure of the influenza virus
The need to provide detailed images of the structure and composition of the C
virus appears to be directly correlated to its transformation into an increasingly
geometric-looking object. On the one hand, these increasingly geometric images respond
to and satisfy the need of clarity required by the specialized discipline and purposes they
have been generated for. On the other hand, this geometric transformation dissociates and
13
strips the virus-turned-object of any expected nature-looking element that connects it to
the biological realm. The above picture becomes a model that stands for all C-virus
objects.
This process confirms that technologies of visualization are, indeed, technologies
of display (Van Loon 2002). In fact, they are geared towards assigning a—possibly clear
and, more importantly, well recognizable—visual profile to viruses, by running the
“object-virus” through a process of increasing rationalization. Simultaneously, they are
technologies of disappearance as they turn the unknown category of infectious agents into
something that is eventually more “obscure,” and that has lost any naturalistic look. In
fact, as the exploration of viruses pushes the boundaries of research towards portraying
smaller and more accurate scales, employing even a vaster range of technological devices
to collect and process data, the viral object is enriched with newer details and can be
analyzed with increasing accuracy. At the same time, the object drifts farther away,
taking appearances that resemble less a natural organism and more a computer generated
or an artificially processed entity.
Visualization and the rendering of scientific data becomes a business of revealing
and "producing scientific reality," rather than copying it (Lynch 2006, 40). This,
according to Lynch, fits the imperatives of science to make reality more understandable
and accessible in a myriad of ways and to a variety of individuals. The operation of
running raw data and blurry images through processing software and turning them into
usable and meaningful pictures, Pawuels suggests, qualify them as “artifact” or as
“creative works (Pawuels 2006).”
It should not come as a surprise to see images such as the one below, in which the
14
colored snapshot of the type C virus displayed in the previous pictures magically appears
on a computer screen to signal the presence of a computer virus or to indicate an infected
computer.
Fig. 6: the influenza virus morphs into a computer virus (courtesy of Bob Rankin blog)
In this case, the appearance of a computer virus is directly borrowed from the
visual vocabulary of microbiology. Why not make such analogy, when both the
methodological process (with its increasing rationalization), and the technologies used
(with their direction towards geometry), encourage such analogy? Since the visual
representation of biological viruses tends towards an increasing informatization, it might
be used to represent computer viruses as well. Biological and computer viruses already
share a few elements: the above image goes a step further and exploits this resemblance.
This, in turn, acknowledges the phenomenon that sees technological devices increasingly
realizing, through visual representation, the assimilation of the two.
Graphing info-viruses.
Visual representational practices not only
"...Represent a phenomenon that is observed in a historical and physical reality
15
(thus implying iconical and indexical reality), but equally may include those
ideographic activities that seek primarily to express ideas or relations and they
have no material referent whatsoever (a combination of mimetic and expressive
elements) (Pawuels 2006, 5)."
When representing informational entities such as computer viruses, the existence of the
referent (the virus constituted of a string of digital information) appears to be only
postulated. Viruses are “enpresented,” that is, they are made insightful, present (Van
Loon 2002, 115). “Enpresenting,” already practiced in the visual representation of
biological viruses, is relevant to describe the process that leads to the representation of
computer viruses.
“In many cases” Trumbo argues, “ scientific information is theoretical or
conceptual with no obvious physical form, so technology, symbolic representation, and
creative expression are employed to craft a visible interpretation” (Trumbo 2006, 267).
The absence of an “obvious physical form” automatically opens to an almost infinite
number of interpretations. This is particularly true in the case of computer viruses, whose
structure and configuration are not directly tied to any physical form, unless this physical
form is extrapolated and rendered by using other types of viruses, usually of the
biological variety, as their designated model. In turn, the physical form biological viruses
are mostly associated with is the fruit of a convergence of interpretations, conjectures and
assumptions on what their form might –or should—looks.
The visual representation of computer viruses is product of the above conjectures,
having to reach for already available images. A comparison occurs between the malware
that ought to be graphically visualized and the scientific visualization of biological
viruses and the latter automatically become the models to be adopted, and adapted to
16
provide the most credible visual appearance. In fact, in the earlier example, the biological
virus was simply "dropped" onto a computer screen. The biological model is used when
malware is processed and translated to generate visuals of computer viruses.
Attempts to visualize computer viruses and worms exploit the tendency of
scientific visualization to partition off their object of study into an increasingly geometric
assemblage, embracing geometric shapes as indicators of their informational nature. The
virtual objects, in this case, emerge from both technological translations and cultural
models that suggest how computer viruses should look.
In September 2005 a 3D animation of the Beagle virus appeared on the F-Secure
weblog, accompanied by the following comment:
"Okay, I hear a lot about these computer viruses but what do they actually look
like?" - goes one of the most frequently asked questions we get. We have been
working on some visualizations projects trying to answer that (Gergo 2005, F-
secure weblog, comment posted September 23)"
Fig.7 visualization and animation of Bagle (F-secure weblog)
The above animation still represents the final phase, of a series of attempts to
graph malware. Initially launched at the 2004 Annual Virus Bulletin conference, the first
17
attempt consisted in utilizing the programming functions of a viral agent as reference
elements for its graphic rendition. This method proposed that viruses be visualized by
reverse engineering their code (Ero 2004, F-secure weblog, comment posted October 24).
Carrera and Erdelyi (2004), two malware analysts at F-Secure, proposed a new
visualization method whereby they located and extracted the functions of the virus known
as MyDoom and re-combined them into a two-dimensional visual map. The graphs were
obtained by “…exploring the code of a malware sample looking for all the functions and
the relationships between them (who calls who) (Carrera and Erdelyi 2004, p.187).”
Fig. 9: Visualization of MyDoom using the graphing technique devised by Carrera and Erdelyi (2004)
Viral code had to go through at least three phases that would first enable the
researchers to analyze data, help them select and classify these data according to a series
of criteria established by the researchers themselves, and, finally, would turn data
collected into graphic elements. The team of analysts responsible for this enterprise had
to decompress and disassemble the malware using a debugging software (IDA Pro)7; the
resulting data had to be prepared to be exported by using a specially designed plug-in
(IDA Python plug-in)8; the prepared material had to be formatted again, through Pydot
18
"..an interface for creating graphs from Python.9" Finally, the processed code could be
uploaded onto the graph visualization software called “GraphViz” that would turn the
data into the above two-dimensional visual map.
The team of researchers recognized that to gain more insights in the structure and
the dynamic functioning of the malware, the data had to be turned into an animation. This
would enable to visualize and follow the various stages of the malware infection,
including what would trigger the code to call each function and how the execution of the
code would unfold. In this second attempt, using the same data extracted and processed
through the same debugging program IDA Pro, the analysts utilized a different plug-in to
format the data and make them suitable for processing them with a 3D software. A
special IDA plug-in programmed using Python was employed to prepare the data to be
uploaded onto the content creation software Blender,10 so that an animation could be
generated.
The boxes in the picture are functions of the worm. The one on the top is the
"main" where the execution starts. The first ring contains all the functions that
"main" calls. The second all the functions that the ones on the first ones call and
so on. All connecting lines represent the calls from one function to the other. Red
boxes belong to the virus code while the blue ones are API calls library code that
do not belong to the malicious code (Gergo 2005, F-secure weblog, comment
posted sept 23)
Visualized using this particular method, the virus unfolds through the web of
relations formed by its active functions. Displayed in the format of an animation, it shows
the progression of a potential viral attack. According to Axelsson, this particular display
of data has utilitarian purposes. In fact, it solves a key problem with current intrusion
detection systems. With the high amount of daily intrusions, typical network security
19
systems have the tendency to produce many false alarms. Applying information
visualization to intrusion detection facilitates the task of the analyst to record the progress
of viral attacks, and to identify weaknesses within specific parts of the system (Axelsson
2007, 80).
The function of the above visual representations and animations to improve the
effectiveness of intrusion detection reflects the double drive that dominates visualization:
improving knowledge by making the object visible. The object visualized becomes more
accessible and manageable. The layers of technologies utilized separate the object from
the user, allowing him/her to observe it from a privileged, safe location. In the
construction of the above animations, lacking any real reference, the representation of the
computer virus tends to use a biological model as its reference.
Although the similarity between these biological and computer of viruses is
somehow assumed, in these examples there is also a clear attempt at underscoring their
differences. The use of rectangular shapes (as opposed to spherical elements) in the
representation of computer viruses reclaim their visual separation from their biological
kins and reflected the urge to establish an iconographic precedent in computer virus
visualization. However, this new visual paradigm had to take into consideration some
existing cultural conventions. In order to be recognizable, the computer virus had to
contain elements that would reestablish a connection to its biological peers: the allocation
of the functions to form a spherical, "virus-reminiscent" form catered to this need. Thus,
driven by the need to assign a visual appearance to a particular type of agent that consists
of code, this visualization had to draw inspiration from the domain of the biological. The
resulting object is more approachable, as it now reveals its functions and active
20
components. At the same time, it becomes more distant, as the code that initially
composed it tends to disappear under layers of processing and visual elements.
The convergence of scientific and information visualization
Interestingly, the two examples reveal how visual representations constitute
important added aesthetic moments.
Pauwels (2006) argues that
…despite a surface appearance of unity and rationality, technoscientific
objectification entails far more ambivalence and insecurity, in which closures are
performed not by following the rules of scientific method but instead by intuition,
symbolic exchange and political association (18).
Transpiring from the above examples in scientific visualization and information
visualization is the evidence that visualizing viral substances involves the intervention of
cultural and aesthetic choices. The images produced are not just a reflection of the
filtering process of the technologies and the instruments used, but also a product of
natural, technological and cultural projections.
In both cases, the substances to be visualized have to undergo a process of
technological translation in order to be turned into visible objects. In addition, any
process of visual representation of biological and computer viruses involves the
concerted intervention of "several actors" (the scientist analyzing and interpreting the
elements captured by the microscope or the data produced by the disassembler, the
programmer building custom software and preparing data for the visualization, the
graphic designer building the model, the professional reading the final image),
"technological devices" (microscopes, computer software, programming language), and
"normative settings" (the resemblance between computer and biological viruses) (29)."
21
Finally, the practice of capturing viruses as agglomerate of data as well as their
reconstruction based on processed information are source of major debates and
confusions between the disciplines of scientific and information visualization. In fact,
because of the size and nature of the object visualized, the reliance on data, and because
the use of computer-generated techniques to process such data are necessary in the visual
construction of both biological and computer viruses, the two disciplines tend to
converge.
As a result of the relative novelty of computer-generated visualization,11 its
principles are still the subject of lengthy discussions. No strict regulations have been
established on how viruses should be represented. On the one hand, the room for
arbitrariness (as well as creative elaboration) potentially increases, leaving room for a
great deal of experimentations. This tendency is compatible with the drive to see farther,
or, to use the metaphor used at the beginning of this paper, to "move the frontier of the
unknown." On the other hand, the lack of strict rules generates some confusions and
debates between disciplines.
Information visualization pictures non-physical information or data that has no
clear mapping to the physical world like stock prices or project tasks, while scientific
visualization pertains to the rendering of data that have a natural or original physical form
(Chen 2005). However, the definition of the latter is open to some ambiguities. The
Georgia Tech Scientific Visualization lab, for example, defines scientific visualization, or
SciVis, in a broader sense as the "representation of data graphically as a means of gaining
understanding and insight into the data (Georgia Tech 2008, online tutorial on scientific
visualization)." This description potentially expands the range of operation of this field to
22
data that don't necessarily have a physical referent.
As a result, there is currently no clear consensus on the boundaries between the
fields of scientific visualization and information visualization. Scholars often use the two
terms interchangeably or they use the first as an umbrella term that contains the second
(Chen 2005). In addition, the increasing reliance of research upon a number of
technological devices that mainly return data upon request, make the two fields even
more blurred.
Attempts to reproduce and visualize viruses further amplify this problem. As the
submicroscopic size and the immateriality of viruses forces scientists and technologists
alike to work mainly with secondary data, the attempts to visualize and to “imagine”
viruses raise issues about the disciplines involved in picturing them and about the rules
that regulate the disciplines themselves. Because the material to be manipulated for the
actual visualization has to consist of data, and because of the iconographic tradition
governing the depiction of viruses (biological or otherwise), the images produced by the
two disciplines often echo each other.
The ambiguity that governs the two fields emerges from the images on display.
The lack of clear directions on how we ought to represent viruses, Chen suggests,
combined with the variety that dominates the fields, is reflected in the production of
pictures where, on the one hand, the recognition of a virus is immediate, thanks to “hints”
that anchor the object display to the general concept of virus. On the other hand, such
identification is partial for a non-expert audience and often confusing for the expert, who
is forced to constantly re-think and reassess his/her competence in interpreting the image.
Ambiguity is also reflected and further promoted through, and thanks to, the
23
variety and diversity of the images produced. The request for images targeted at general
education or to be used as illustrations on covers of magazines and books is increasing. In
addition to stimulating a continuous development of imaging technologies and computer
graphics, this production has fostered the rise of a growing industry specialized in
scientific and information visualization started offering images that cater to biomedical,
entertainment and professional markets, with the goal of providing “innovative creative
development, artistic quality and service (3D science 2007).”
This confirms the inherent value of viruses as undetermined agents, as entities
that embody the unknown. The race to delimit and clearly delineate viruses through
visualization reiterates a drive to move away the horizon of the unknown as well as a
need to exorcize it. The technologies used to produce visual representations of viruses
simultaneously approach and withdraw from the virus-turned-object.
References
Blender. 2009Available from http://www.blender.org/.
Pydot. 2008Available from http://dkbza.org/pydot.html.
3D science. 2007Available from http://www.3dscience.com/Company_Info.php.
IDA-pro. 2008]. Available from http://www.hex-rays.com/idapro/.
Abrams, Randy. 2006. Everything you wanted to know about computer
viruses....MALWARE... but you were afraid you might get infected if you asked .
Nercomp Northeast regional computing programs.
Axelsson, Stefan, and David Sands. 2006. Understanding intrusion detection through
visualization. Advances in information security. Vol. 24. New York: Springer.
Bauman, Zygmunt. 2006. Liquid fear. Cambridge ; Malden, Mass.: Polity Press.
24
Bear, Greg. 1986. Blood music. New York: Ace Books.
Card, Stuart K. 1999. Readings in information visualization : Using vision to think, eds.
Jock D. Mackinlay, Ben Shneiderman. San Francisco, Calif.: Morgan Kaufmann
Publishers.
Carrera, Ero, and Gergely Erdélyi. 2004. Digital genome mapping: Advanced binary
malware analysis. Paper presented at Virus Bulletin Conference, Chicago Il, 29 Sept.
Oct. 1.
Chen, Chaomei 2005. Top 10 unsolved information visualization problems. IEEE
Computer Graphics and Applications 25, (4) (July 2005): 12-6.
Chen, Chaomei 2004. Information visualization : Beyond the horizon. New York:
Springer.
Coy, Peter. 1996. 10/28/96 andersen consulting: Data visualization: The final frontier?
Businnesweek.
Ero. October 24, 2004. Graphing malware.
Sean. 2007 July 5. FSCSI and Visualization Tools. http://www.f-
secure.com/weblog/archives/00001225.html.
Galloway, Alexander R. 2007. The exploit : A theory of networks, ed. Eugene Thacker.
Minneapolis: University of Minnesota Press.
Gergo. 2005 sept 23. A different look at bagle. http://www.f-
secure.com/weblog/archives/archive-092005.html#00000662 ed.
Graphviz. Http://www.graphviz.org/. 2008.
ICTVdb. Influenza C virus. 2008]. Available from
http://www.ncbi.nlm.nih.gov/ICTVdb/ICTVdB/00.046.0.02.htm.
In-Vsee. Interactive nano-visualization in science and engineering in education. 2008.
Kaspersky, Eugene. The contemporary antivirus industry and its problems. Available
from http://www.kaspersky.com/eugenearticle (accessed 4/22/2008).
25
Latour, Bruno 1983. Give me a laboratory and I will raise the world. In The science
studies reader., ed. M. Biagioli, 258-275. New York: Routledge.
Lynch, Michael 2006. Discipline and the material form of images : An analysis of
scientific visibility. In Visual cultures of science : Rethinking representational
practices in knowledge building and science communication., ed. L. Pauwels, 195-
221. Hanover, NH: Dartmouth College Press.
Pasveer, Bernicke. 2006. Representing or mediating : A history and philosophy of X-ray
images in medicine. In Visual cultures of science : Rethinking representational
practices in knowledge building and science communication., ed. L. Pauwels, 41-62.
Hanover, NH: Dartmouth College Press.
Pauwels, Luc 2006. A theoretical framework for assessing visual representational
practices in knowledge building and science communications. In Visual cultures of
science : Rethinking representational practices in knowledge building and science
communication., ed. L. Pauwels, 1-25. Hanover, NH: Dartmouth College Press.
Robins, Kevin. 1996. Into the image : Culture and politics in the field of vision. London ;
New York: Routledge.
Scientific visualization laboratory. What is scientific visualization? 2008. Available from
http://www.cc.gatech.edu/scivis/tutorial/linked/whatisscivis.html.
Siggraph. History of visualization. 2008. Available from
http://www.siggraph.org/education/materials/HyperVis/visgoals/visgoal3.htm.
Stafford, Barbara Maria. 1996. Good looking : Essays on the virtue of images.
Cambridge, Mass.: The MIT Press.
Stephenson, Neal. 1993. Snow crash. A bantam spectra book. Bantam paperback ed. New
York: Bantam Books.
Trumbo, Jean. 2006. Making science visible : Visual literacy in science communication.
In Visual cultures of science : Rethinking representational practices in knowledge
building and science communication., ed. L. Pauwels, 226-284. Hanover, NH:
Dartmouth College Press.
van Loon, Joost. 2002. A contagious living fluid. objectification and assemblage in the
history of virology. Theory, Culture and Society 19, (5/6): 107-24.
26
Virilio, P. 2000. The information bomb. New York: Verso.
Wagner, Edward K., and Martinez J. Hewlett. 2004. Basic virology. 2nd ed. Malden,
MA: Blackwell Science.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
1 In both Snow Crash (Neal Stephenson) and Blood Music (Greg Bear, see the short story in 1983- and the
novel in 1985) computer viruses end up affecting human characters.
2 At In-Vsee for instance, (the interactive nano-visualization in science and engineering in education),
“Space has often been called the "final frontier" but the micro/nano universe is an equally unknown frontier
that exists all around us right here on earth. In exploring outer space we probe the secrets of galaxies, stars
and planets with telescopes. In exploring micro/nano space we probe the secrets of cells, molecules and
atoms with microscopes. Often times, discoveries made in micro/nano space can teach us about outer space
and vice-versa (In-Vsee 2008).”
3 This attitude was also visible in the attempts to classify viruses according to am identical taxonomical
technique. In doing so, viruses have to be separated from their host, despite the fact that their active status
lies in this stage.
4 In the F-Secure animated study of the virus "Bagle" the virus is isolated from the rest of the digital
environment and displayed on a dark background. While this choice could be dictated by the need for
clarity and may respond to the specific goals that require that only one specific virus be displayed, it can
also be interpreted as the product of the in-vitro habit to approach viruses and viral substances (F-Secure
2007).
5 There are two types of electron microscope, returning quite different images. The SEM (Scanning
Electron Microscope) returns a 3D image. The TEM (Transmission Electron Microscope) gives a 2D view.
In both cases, the resulting image is in Black and White. The procedure described by Wagner utilizes a
SEM microscope.
6 The ICTV database records 3 types of influenza virus classified as A, B, and C. the latter is usually
present in human beings and pigs, and seldom causes grave pandemics (ICTVbd 2008)
7 Quoting from the website of its distributor, IDA pro is a "..Windows or Linux hosted multi-processor
disassembler (IDA-pro 2008)." DataRescue first developed and published this disassembler from late 1995
to Jan 2008.
8 IDAPython is an IDA Pro plugin that integrates the Python programming language, allowing scripts to
run in IDA Pro.
9 Python interface to Graphviz's Dot language.
10 Blender is a free open source 3D content creation suite, available for all major operating systems under
the GNU General Public License (Blender 2008). Apparently, this was the best and most convenient option
available to the team of reasearchers: Blender supports Python extensions.
11 According to SIGGRAPH, leading conference in Computer Graphics "...recent emphasis on visualization
started in 1987 with the special issue of Computer Graphics, [MCCO87], on Visualization in Scientific
Computing (Siggraph 2008)."
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
From the Publisher: Into the Image is concerned with the significance of screen and image in contemporary society, and with the nature of our imaginary and psychic investments in visual culture. It considers modern image technologies as means to monitor and survey the world, whilst at the same time maintaining distance and detachment from it. In the coverage of contemporary war, we see most clearly how the world is screened and yet its reality screened out. Into the Image also reflects on the contemporary desire to create an alternative world by means of new image technologies. It asks what is behind the fantasies of migrating into an alternative, virtual reality. Critical of the dominant technoculture, this book seeks to develop an alternative approach to visual culture based in the realities of the contemporary social order. In its exploration of culture and politics in the field of vision, Into the Image acknowledges the continuing significance of the 'old' technologies of photography, cinema and television alongside that of the new digital developments. The crucial issues, it argues, concern the relation of image and screen culture to experience in the modern world.
Book
Full-text available
Information visualization is not only about creating graphical displays of complex and latent information structures. It contributes to a broader range of cognitive, social, and collaborative activities. This is the first book to examine information visualization from this perspective. This 2nd edition continues the unique and ambitious quest for setting information visualization and virtual environments in a unifying framework. It pays special attention to the advances made over the last five years and the potentially fruitful directions to pursue. It is particularly updated to meet the need for practitioners. The book is a valuable source for researchers and graduate students. Its key features include the latest advances in information visualization, and applications of information visualization, including knowledge domain visualization, knowledge diffusion, and social networks. This new edition is forwarded by Ben Shneiderman of the University of Maryland.
Article
This article deals with the birth of `the virus' as an object of technoscientific analysis. The aim is to discuss the process of objectification of pathogen virulence in virological and medical discourses. Through a short excursion into the history of modern virology, it will be argued that far from being a matter of fact, pathogen virulence had to be `produced', for example in petri-dishes, test-kits and hyper-real signification-practices. The now commonly accepted objective status of `the virus' has been an accomplishment of a complex ensemble of actors. Indeed, this illustrates why objectification rather than objectivity has become the main focus of science and technology studies. The objectification of `the' virus was by no means a smooth process. It involved more than five decades of highly speculative and fragmented research projects before it became actualized as a separate discipline under the heading of virology. The specific objectification of viruses took place through an inter-disciplinary de-differentiation of research questions, methodologies, techniques and technologies. The main argument of this article is that viruses only became intelligible after the establishment of a virology-assemblage. Its inauguration in the early 1950s was radical and sudden because only then could the various substrands of virological technoscience affect each other through deliberate enrolment, and engender a universal intelligibility.
Article
This paper is about how natural objects are made visible and analyzable in scientific research. It is argued that the objects scientists actually work upon are highly artificial, in that their visibility depends upon complex instruments and careful preparatory procedures. Instruments and laboratory procedures do more than provide a window to the world; they lay the groundwork for specific analytic operations which utilize literary resources to represent phenomena graphically. Two specific cases from biology are discussed. The first is from a popular field manual, and is used to introduce themes for analyzing a more complex case, a neuroscience project using electron microscopy of brain tissue. The discussion of both cases concerns how specimens are modified into `docile objects' for purposes of investigation. These modifications are summarized under the headings of `marking', `constituting graphic space', and `normalizing observations'. Finally, it is claimed that these practices make up an `externalized retina' for scientific perception — a `retina' that depends upon disciplined conduct within the laboratory setting.
Article
Images of science are evident throughout the media, with new technologies playing an important role in allowing the creation of science representation by communication practitioners, scientists, and the public. The role of visual literacy as a key ingredient in the effective communication of science among expert and lay audiences is explored, and a framework for addressing visual literacy is suggested. Visual literacy is defined in this context as a holistic construct that includes visual thinking, visual learning, and visual communication.