Graph drawing by force-directed placement
Software—Practice & Experience
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Visualization Techniques for Mining Large Databases: A Comparison
IEEE Transactions on Knowledge and Data Engineering
FADE: Graph Drawing, Clustering, and Visual Abstraction
GD '00 Proceedings of the 8th International Symposium on Graph Drawing
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data
IEEE Transactions on Visualization and Computer Graphics
Visual Analytics: Scope and Challenges
Visual Data Mining
Scalable graph clustering using stochastic flows: applications to community discovery
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A strategic analysis of spam botnets operations
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
VisBricks: Multiform Visualization of Large, Inhomogeneous Data
IEEE Transactions on Visualization and Computer Graphics
Angular Histograms: Frequency-Based Visualizations for Large, High Dimensional Data
IEEE Transactions on Visualization and Computer Graphics
DICON: Interactive Visual Analysis of Multidimensional Clusters
IEEE Transactions on Visualization and Computer Graphics
Industrial espionage and targeted attacks: understanding the characteristics of an escalating threat
RAID'12 Proceedings of the 15th international conference on Research in Attacks, Intrusions, and Defenses
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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In this work we present a visual analytics tool introducing a new kind of graph visualization that exploits the nodes' degree to provide a simplified and more abstract, yet accurate, representation of the most important elements of a security data set and their inter-relationships. Our visualization technique is designed to address two shortcomings of existing graph visualization techniques: scalability of visualization and comprehensibility of results. The main goal of our visual analytics tool is to provide security analysts with an effective way to reason interactively about various attack phenomena orchestrated by cyber criminals. We demonstrate the use of our tool on a large corpus of spam emails by visualizing spam campaigns performed by spam botnets. In particular, we focus on the analysis of spam sent in March 2011 to understand the impact of the Rustock takedown on the botnet ecosystem. As spam botnets continue to play a significant role in the worldwide spam problem, we show with this application how security visualization based on abstract graphs can help us gain insights into the strategic behavior of spam botnets, and a better understanding of large-scale spammers operations.