Network Security. Data Visualization . courtdadelorec.tk~owen/ Research/Conference%20Publications/honeynet_IAWpdf. 0. Security Visualization. Past Ben Shneiderman, The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Security Visualization and Enabler Books Emerge courtdadelorec.tk . data visualization tools to your process. Greg Conti, in his groundbreaking gem, Security Data Visualization: Graphical. Techniques for Network Analysts, sums it .
|Language:||English, Spanish, Japanese|
|Distribution:||Free* [*Register to download]|
PDF | Networked computers are ubiquitous, and are subject to attack, misuse, and ply, information visualization turns data into interactive graphical displays. Security data visualization also plays key role in emerging fields such as data science courtdadelorec.tk Gather Raw Network. Data .  Greg Conti. Security Data Visualization: Graphical Techniques for Network Analysis.
Visualizing Deep Neural Networks for Text Analytics Visualizing a convolutional neural network model for part-of-speech tagging, with the word visualization as input. Deep neural networks DNNs have made tremendous progress in many different areas in recent years. How these networks function internally, however, is often not well understood.
Advances in understanding DNNs will benefit and accelerate the development of the field. We present TNNVis, a visualization system that supports understanding of deep neural networks specifically designed to analyze text. It integrates visual encodings and interaction techniques chosen specifically for our tasks. The tool allows users to: 1 visually explore DNN models with arbitrary input using a combination of node—link diagrams and matrix representation; 2 quickly identify activation values, weights, and feature map patterns within a network; 3 flexibly focus on visual information of interest with threshold, inspection, insight query, and tooltip operations; 4 discover network activation and training patterns through animation; and 5 compare differences between internal activation patterns for different inputs to the DNN.
These functions allow neural network researchers to examine their DNN models from new perspectives, producing insights on how these models function. Clustering and summarization techniques are employed to support large convolutional and fully connected layers. Based on several part of speech models with different structure and size, we present multiple use cases where visualization facilitates an understanding of the models.
Dengue Fever Surveillance in India Using Text Mining in Public Media Despite the improvement in health conditions across the world during the past decades, communicable diseases remain among the leading mortality causes in many countries.
Combating communicable diseases depends on surveillance, preventive measures, outbreak investigation and the establishment of control mechanisms. Delays in obtaining country level data of confirmed communicable diseases cases, like dengue fever, are prompting new efforts for short- to medium-term data.
News articles highlight dengue infections and they can reveal how public health messages, expert findings, and uncertainties are communicated to the public. In this paper, we analyze dengue news articles in Asian countries, with a focus in India, for each month in We investigate how the reports cluster together, and uncover how dengue cases, public health messages and research findings are communicated in the press.
Our main contributions are to: 1 uncover underlying topics from news articles that discuss dengue in Asian countries in ; 2 construct topic evolution graphs through the year; and 3 analyze the life cycle of dengue news articles in India, then relate them to rainfall, monthly reported dengue cases, and the Breteau Index. We show that the five main topics discussed in the newspapers in Asia in correspond to: 1 prevention; 2 reported dengue cases; 3 politics; 4 prevention relative to other diseases; and 5 emergency plans.
We identify that rainfall has 0. Based in our findings, we conclude that the proposed method facilitates in the effective discovery of evolutionary dengue themes and patterns. Villanes, A. The Utility of Beautiful Visualizations Geovisualizations provide a means to inspect large complex multivariate datasets for information that would not otherwise be available with a tabular view or summary statistics alone. Aesthetically appealing visualizations can elicit prolonged exploration and encourage discovery.
Creating data geovisualizations that are effective and beautiful is an important yet difficult challenge. Here we present a tool for rendering geovisualizations of continuous spatial data using impressionist painterly techniques. The techniques, which have been tested in controlled studies, vary the visual properties e. To demonstrate this technique, we render two examples: 1 weather data attributes e.
These examples demonstrate how open source geospatial visualizations can harness aesthetics to enhance visual communication and viewer engagement. Tateosian, L. A tracking and automatic color adjustment system are designed so that the device can work robustly with noisy surroundings and is invariant to changes in lighting and background noise. A user study of 3D rotation tasks shows that the device outperforms other 6 DoF input devices used in a similar desktop environment.
The device has the potential to facilitate interactive applications such as games as well as viewing 3D information. Chen, Z. Amant, R. Real-Time Independent Rasterization for Multi-View Rendering Multi-view soft shadows rendered using: left traditional multi-pass rasterization; right view-independent rasterization VIR paired with parallel view rendering, both methods produce high quality shadow penumbra, but VIR requires only a fraction of the time Existing graphics hardware parallelizes view generation poorly, placing many multi-view effects—such as soft shadows, defocus blur, and reflections—out of reach for real-time applications.
We present emerging solutions that address this problem using a high density point set tailored per frame to the current multi-view configuration, coupled with relatively simple reconstruction kernels.
Points are a more flexible rendering primitive, which we leverage to render many high resolution views in parallel. Marrs, A. Large Image Collection Visualization Using Perception-Based Similarity with Color Features This paper introduces the basic steps to build a similarity-based visualization tool for large image collections. We build the similarity metric s based on human perception. Psychophysical experiments have shown that human observers can recognize the gist of scenes within milliseconds msec by comprehending the global properties of an image.
Color also plays an important role in human rapid scene recognition. However, previous works often neglect color features. We propose new scene descriptors that preserve the information from coherent color regions, as well as the spatial layouts of scenes.
Experiments show that our descriptors outperform existing state-of-the-art approaches. Given the similarity metrics, a hierarchical structure of an image collection can be built in a top-down manner. Representative images are chosen for image clusters and visualized using a force-directed graph. Applying Impressionist Painterly Techniques to Data Visualization An important task of science is to communicate complex data to peers and the public. Here we ask whether harnessing the painterly techniques of impressionist-era painters is beneficial.
In two experiments, participants viewed weather maps from the International Panel of Climate Change that were rendered using either an industry-standard technique glyphs or one of three styles inspired from impressionist masters. The glyph technique used rectangular glyphs that vary properties of color and texture e. For the impressionist styles, regions of maximum contrast in the underlying data were rendered using brushstroke algorithms to emphasize interpretational complexity two distinct layers of paint where unique regions have greater brushstroke overlap , indication and detail unique regions are rendered with increased brushstroke thickness and density , and visual complexity unique regions are rendered with different brushstrokes at a global level and reinforced with increased brushstroke variation at a local level.
Visual complexity was expected to be more memorable and allow for more accurate information extraction because it both draws attention to distinct image regions and engages the viewer at those locations with increased brushstroke variability.
In Experiment 1 thirty participants completed a new—old recognition test for which d-prime values of visual complexity and glyph were comparable, and both superior to the other styles. Experiment 2 tested the accuracy of numerosity estimation with a different group of thirty participants and here visual complexity was superior above all other styles.
An exit poll completed at the end of both studies further revealed that the style participants identified as being "most liked" associated with higher performance relative those not selected. Incidental eye-tracking revealed impressionist styles elicited greater visual exploration over glyphs. These results offer a proof-of-concept that visualizations based on Impressionist brushstrokes can be memorable, functional, and engaging.
Pete Beach, FL 16, 12, , Visualizing Static Ensembles for Effective Shape and Data Comparison The challenges of cyber situation awareness call for ways to provide assistance to analysts and decision-makers. In many fields, analyses of complex systems and activities benefit from visualization of data and analytical products.
Analysts use images in order to engage their visual perception in identifying features in the data, and to apply the analysts' domain knowledge. One would expect the same to be true in the practice of cyber analysts as they try to form situational awareness of complex networks. This chapter takes a close look at visualization for Cyber Situation Awareness. We begin with a basic overview of scientific and information visualization, and of recent visualization systems for cyber situation awareness.
Then, we outline a set of requirements, derived largely from discussions with expert cyber analysts, for a candidate visualization system. Hao, L. Effective Visualization of Temporal Ensembles An ensemble is a collection of related datasets, called members, built from a series of runs of a simulation or an experiment.
Ensembles are large, temporal, multidimensional, and multivariate, making them difficult to analyze. Another important challenge is visualizing ensembles that vary both in space and time.
Initial visualization techniques displayed ensembles with a small number of members, or presented an overview of an entire ensemble, but without potentially important details. Recently, researchers have suggested combining these two directions, allowing users to choose subsets of members to visualization.
This manual selection process places the burden on the user to identify which members to explore. We first introduce a static ensemble visualization system that automatically helps users locate interesting subsets of members to visualize. We next extend the system to support analysis and visualization of temporal ensembles.
We employ 3D shape comparison, cluster tree visualization, and glyph based visualization to represent different levels of detail within an ensemble. This strategy is used to provide two approaches for temporal ensemble analysis: 1 segment based ensemble analysis, to capture important shape transition time-steps, clusters groups of similar members, and identify common shape changes over time across multiple members; and 2 time-step based ensemble analysis, which assumes ensemble members are aligned in time by combining similar shapes at common time-steps.
Both approaches enable users to interactively visualize and analyze a temporal ensemble from different perspectives at different levels of detail. We demonstrate our techniques on an ensemble studying matter transition from hadronic gas to quark-gluon plasma during gold-on-gold particle collisions.
Ensemble Visualization for Cyber Situation Awareness of Network Security Data Network security analysis and ensemble data visualization are two active research areas.
Although they are treated as separate domains, they share many common challenges and characteristics. Both focus on scalability, time-dependent data analytics, and exploration of patterns and unusual behaviors in large datasets. These overlaps provide an opportunity to apply ensemble visualization research to improve network security analysis. To study this goal, we propose methods to interpret network security alerts and flow traffic as ensemble members.
We can then apply ensemble visualization techniques in a network analysis environment to produce a network ensemble visualization system. Including ensemble representations provide new, in-depth insights into relationships between alerts and flow traffic. Analysts can cluster traffic with similar behavior and identify traffic with unusual patterns, something that is difficult to achieve with high-level overviews of large network datasets. Furthermore, our ensemble approach facilitates analysis of relationships between alerts and flow traffic, improves scalability, maintains accessibility and configurability, and is designed to fit our analysts' working environment, mental models, and problem solving strategies.
Visualizations and Analysts The challenges of cyber situation awareness call for ways to provide assistance to analysts and decision-makers. Healey, C. Kott, C.
Wang and R. Erbacher, Eds. Visualizing Likelihood Density Functions via Optimal Region Projection Effective visualization of high-likelihood regions of parameter space is severely hampered by the large number of parameter dimensions that many models have. We present a novel technique, Optimal Percentile Region Projection, to visualize a high-dimensional likelihood density function that enables the viewer to understand the shape of the high-likelihood region.
Optimal Percentile Region Projection has three novel components: first, we select the region of high likelihood in the high-dimensional space before projecting its shadow into a lower-dimensional projected space.
Second, we analyze features on the surface of the region in the projected space to select the projection direction that shows the most interesting parameter dependencies. Finally, we use a three-dimensional projection space to show features that are not salient in only two dimensions. The viewer can also choose sets of axes to project along to explore subsets of the parameter space, using either the original parameter axes or principal-component axes.
The technique was evaluated by our domain-science collaborators, who found it to be superior to their existing workflow both when there were interesting dependencies between parameters and when there were not. Canary, H. Flexible Web Visualization for Alert-Based Network Security Analytics This paper describes a web-based visualization system designed for network security analysts at the U. Our goal is to provide visual support to the analysts as they investigate security alerts for malicious activity within their systems.
We describe key elements of our design, explain how an analyst's intent is used to generate different visualizations, and show how the system's interface allows an analyst to rapidly produce a sequence of visualizations to explore specific details about a potential attack as they arise.
We conclude with a discussion of plans to further improve the system, and to collect feedback from our ARL colleagues on its strengths and limitations in real-world analysis scenarios. On the Limits of Resolution and Visual Angle in Visualization This article describes a perceptual level-of-detail approach for visualizing data. Properties of a dataset that cannot be resolved in the current display environment need not be shown, for example, when too few pixels are used to render a data element, or when the element's subtended visual angle falls below the acuity limits of our visual system.
To identify these situations, we asked: 1 What type of information can a human user perceive in a particular display environment? To answer these questions, we conducted controlled experiments that identified the pixel resolution and subtended visual angle needed to distinguish different values of luminance, hue, size, and orientation.
This information is summarized in a perceptual display hierarchy, a formalization describing how many pixels—resolution—and how much physical area on a viewer's retina—visual angle—is required for an element's visual properties to be readily seen.
We demonstrate our theoretical results by visualizing historical climatology data from the International Panel for Climate Change. Interest Driven Navigation in Visualization This paper describes a new method to explore and discover within a large dataset. We apply techniques from preference elicitation to automatically identify data elements that are of potential interest to the viewer.
These "elements of interest" are bundled into spatially local clusters, and connected together to form a graph. The graph is used to build camera paths that allow viewers to "tour" areas of interest within their data. It is also visualized to provide wayfinding cues. Our preference model uses Bayesian classification to tag elements in a dataset as interesting or not interesting to the viewer.
The model responds in real-time, updating the elements of interest based on a viewer's actions. This allows us to track a viewer's interests as they change during exploration and analysis. Viewers can also interact directly with interest rules the preference model defines.
We demonstrate our theoretical results by visualizing historical climatology data collected at locations throughout the world. Attention and Visual Memory in Visualization and Computer Graphics A change blindness example, it is often difficult to immediately see the difference between the left and the right images. Once found, it is clear the difference is not subtle. Limits on visual memory make it difficult to compare the images.
A fundamental goal of visualization is to produce images of data that support visual analysis, exploration, and discovery of novel insights. An important consideration during visualization design is the role of human visual perception. This article surveys research on attention and visual perception, with a specific focus on results that have direct relevance to visualization and visual analytics.
We discuss theories of low-level visual perception, then show how these findings form a foundation for more recent work on visual memory and visual attention. We conclude with a brief overview of how knowledge of visual attention and visual memory is being applied in visualization and graphics.
We also discuss how challenges in visualization are motivating research in psychophysics. Exploring Ensemble Visualization An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional, and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter choices affect the simulation.
To draw inferences from an ensemble, scientists need to compare data both within and between ensemble members. We propose two techniques to support ensemble exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision.
We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of ensemble data. Phadke, M. Comparative Visualization of Ensembles Using Ensemble Surface Slicing By definition, an ensemble is a set of surfaces or volumes derived from a series of simulations or experiments. Sometimes the series is run with different initial conditions for one parameter to determine parameter sensitivity.
The understanding and identification of visual similarities and differences among the shapes of members of an ensemble is an acute and growing challenge for researchers across the physical sciences.
More specifically, the task of gaining spatial understanding and identifying similarities and differences between multiple complex geometric data sets simultaneously has proved challenging. This paper proposes a comparison and visualization technique to support the visual study of parameter sensitivity. ESS produces a single image that is useful for determining differences and similarities between surfaces simultaneously from several data sets.
We demonstrate the usefulness of ESS on two real-world data sets from our collaborators. Alabi, O. Visualizing Combinatorial Auctions Visualizing three stages in a combinatorial auction: concentric rings represent different bundles of goods, segment color and blur shows bid price and interest in a bundle, and white rectangles identify a "winning" bidder for a bundle; winning bids connected with dashed lines identify a competitive allocation of all goods in the auction We propose a novel scheme to visualize combinatorial auctions; auctions that involve the simultaneous sale of multiple items.
Buyers bid on complementary sets of items, or bundles, where the utility of securing all the items in the bundle is more than the sum of the utility of the individual items. Our visualizations use concentric rings divided into arcs to visualize the bundles in an auction.
Keyframe animations are used to show changes in an auction over time. We demonstrate our visualization technique on a standard testbed dataset generated by researchers to evaluate combinatorial auction bid strategies, and on recent Federal Communications Commission FCC auctions designed to allocate wireless spectrum licenses to cell phone service providers.
Hsiao, J. Interactive Visual Summarization of Multidimensional Data Visualization has become integral to the knowledge discovery process across various domains. However, challenges remain in the effective use of visualization techniques, especially when displaying, exploring and analyzing large, multidimensional datasets, such as weather and meteorological data.
Direct visualizations of such datasets tend to produce images that are cluttered with excess detail and are ineffective at communicating information at higher levels of abstraction.
To address this problem we provide a visual summarization framework to intuitively reduce the data to its important and relevant characteristics. Summarization is performed in three broad steps. Next, patterns, relationships and outliers are extracted from the reduced data. Finally, the extracted summary characteristics are visualized to the user.
Such visualizations reduce excess visual detail and are more suited to the rapid comprehension of complex data. Users can interactively guide the summarization process gaining insight into both how and why the summary results are produced.
Our framework improves the benefits of mathematical analysis and interactive visualization by combining the strengths of the computer and the user to generate high-quality summaries. Initial results from applying our framework to large weather datasets have been positive, suggesting that our approach could be beneficial for a wide range of domains and applications.
Kocherlakota, S. Visual Perception and Mixed-Initiative Interaction For Assisted Visualization Design This paper describes the integration of perceptual guidelines from human vision with an AI-based mixed-initiative search strategy.
The result is a visualization assistant called ViA, a system that collaborates with its users to identify perceptually salient visualizations for large, multidimensional datasets. ViA applies knowledge of low-level human vision to: 1 evaluate the effectiveness of a particular visualization for a given dataset and analysis tasks; and 2 rapidly direct its search towards new visualizations that are most likely to offer improvements over those seen to date.
Context, domain expertise, and a high-level understanding of a dataset are critical to identifying effective visualizations. We apply a mixed-initiative strategy that allows ViA and its users to share their different strengths and continually improve ViA's understanding of a user's preferences.
We visualize historical weather conditions to compare ViA's search strategy to exhaustive analysis, simulated annealing, and reactive tabu search, and to measure the improvement provided by mixed-initiative interaction.
We also visualize intelligent agents competing in a simulated online auction to evaluate ViA's perceptual guidelines. Results from each study are positive, suggesting that ViA can construct high-quality visualizations for a range of real-world datasets. Visualizing Multidimensional Query Results Using Animation Effective representation of large, complex collections of information datasets presents a difficult challenge.
Visualization is a solution that uses a visual interface to support efficient analysis and discovery within the data. Our primary goal in this paper is a technique that allows viewers to compare multiple query results representing user-selected subsets of a multidimensional dataset.
We present an algorithm that visualizes multidimensional information along a space-filling spiral. Graphical glyphs that vary their position, color, and texture appearance are used to represent attribute values for the data elements in each query result. Guidelines from human perception allow us to construct glyphs that are specifically designed to support exploration, facilitate the discovery of trends and relationships both within and between data elements, and highlight exceptions.
A clustering algorithm applied to a user-chosen ranking attribute bundles together similar data elements. This encapsulation is used to show relationships across different queries via animations that morph between query results.
We apply our techniques to the MovieLens recommender system, to demonstrate their applicability in a real-world environment, and then conclude with a simple validation experiment to identify the strengths and limitations of our design, compared to a traditional side-by-side visualization.
Sawant, A. ChipViz: Visualizing Memory Chip Test Data This paper presents a technique that allows test engineers to visually analyze and explore within memory chip test data. We represent the test results from a generation of chips along a traditional grid and a spiral. We also show correspondences in the test results across multiple generations of memory chips.
We use simple geometric "glyphs" that vary their spatial placement, color, and texture properties to represent the critical attribute values of a test. When shown together, the glyphs form visual patterns that support exploration, facilitate discovery of data characteristics, relationships, and highlight trends and exceptions in the test data that are often difficult to identify with existing statistical tools. Weaving Versus Blending: A Quantitative Assessment of the Information Carrying Capacities of Two Alternative Methods for Conveying Multivariate Data With Color In many applications, it's important to understand the individual values of, and relationships between, multiple related scalar variables defined across a common domain.
Found a nice collection of network attack maps. In the presentation I look at a number of topics around using big data for security. I start by showing what big data looks like for security, how the history of using security for big data is tightly linked to the progress in big data itself. I talk about machine learning and artificial intelligence and show some of the limits and dangers of how we currently apply machine learning in security and how we can apply data visualization to help analysts better understand data.
I then go on to peek a little bit into my magic 8 ball to see how security big data environments might look in the future and finish the presentation with posing a few challenges to the community about security for big data problems. I have a questionnaire, for my thesis, aimed at people who have experience in Cyber Security, Visualization or HCI design or both. I would really appreciate if you can take some time out and fill out the questionnaire.
Big data and security intelligence are the two very hot topics in security. We are collecting more and more information from both the infrastructure, but increasingly also directly from our applications. This vast amount of data gets increasingly hard to understand. Terms like map reduce, hadoop, spark, elasticsearch, data science, etc. But what are those technologies and techniques? We will see that none of these technologies are sufficient in our quest to defend our networks and information.
Data visualization is the only approach that scales to the ever changing threat landscape and infrastructure configurations. Using big data visualization techniques, you uncover hidden patterns of data, identify emerging vulnerabilities and attacks, and respond decisively with countermeasures that are far more likely to succeed than conventional methods.
Something that is increasingly referred to as hunting. The attendees will learn about log analysis, big data, information visualization, data sources for IT security, and learn how to generate visual representations of IT data. The workshop is being heavily updated over the next months. Check back here to see a list of new topics:. The section on big data is covering the following: Raffael Marty is vice president of security analytics at Sophos, and is responsible for all strategic efforts around security analytics for the company and its products.
He is based in San Francisco, Calif. Marty is one of the world's most recognized authorities on security data analytics, big data and visualization.
His team at Sophos spans these domains to help build products that provide Internet security solutions to Sophos' vast global customer base.
Previously, Marty launched pixlcloud, a visual analytics platform, and Loggly, a cloud-based log management solution. With a track record at companies including IBM Research, ArcSight, and Splunk, he is thoroughly familiar with established practices and emerging trends in the big data and security analytics space.
Marty is the author of Applied Security Visualization and a frequent speaker at academic and industry events. Zen meditation has become an important part of Raffy's life, sometimes leading to insights not in data but in life. We recently posted a case study of how a Fortune company is using Security Visualization as a front end to their various data collection systems. The Security Visualization allows the company's analysts to look at 's of thousands of correlations each day and apply human pattern recognition to spot the "needles in the haystack".
These are threats that are designed to avoid traditional intrusion and event management.
Once the potential threat is identified and the log data is carved down to just the logs that are relevant, that subset of log data is then attached to a case study and delivered to case investigation for further evaluation.
In addition to identifying and carving down to just the relevant logs, the security visualization also makes it easier to communicate the findings to the extended team. In this situation data is imported from several sources. Those sources include intrusion detection systems e. Symantec in addition to correlation systems e. Security Visualization allows the analysts to hunt for unknown and unexpected threats. Threats such as time staged attacks, diagonal attacks, cluster attacks, octal jump attacks, embedded activity attacks, etc.
This case study is recorded and can be viewed at http: I prepared an online survey as a part of my phd thesis. However, since this subject is relatively new I can not find anybody who may fill this survey around me in Turkey.
The survey is in Google Forms, at link https: It is not very short: It may take around 20 minutes but it is easy to fill, mostly composed of multi selection questions. Uncompleted survey results are not saved so the participants should complete the survey. Although we ask questions related to security systems and security visualization systems used to understand the visualization requirements.
The survey, in general, does not include questions that give personal discomfort. No tracking information such as email or organization name is asked during the survey. More descriptive information about how the survey results will be used exists in the starting page.
So, please do not hesitate to fill, due to your privacy concerns. I hope experts of this forum may help me by filling the survey during a coffee break. I need to take feedback soon, before my next thesis committee. I appreciate your help to a newbie security visualization researcher me: The 13th IEEE Symposium on Visualization for Cyber Security VizSec is a forum that brings together researchers and practitioners from academia, government, and industry to address the needs of the cybersecurity community through new and insightful visualization and analysis techniques.
VizSec provides an excellent venue for fostering greater exchange and new collaborations on a broad range of security- and privacy-related topics. The purpose of VizSec is to explore effective and scalable visual interfaces for security domains such as network security, computer forensics, reverse engineering, insider threat detection, cryptography, privacy, user assisted attacks prevention, compliance management, wireless security, secure coding, and penetration testing.
Full papers describing novel contributions in security visualization are solicited. Papers may present techniques, applications, practical experience, theory, analysis, experiments, or evaluations. We encourage the submission of papers on technologies and methods that promise to improve cyber security practices, including, but not limited to:.
Short papers describing practical applications of security visualization are solicited. We encourage the submission of papers discussing the introduction of cyber security visualizations into operational context, including, but not limited to:. Cyber security practitioners from industry, as well as the research community, are encouraged to submit case studies.