Visual clustering of spam emails for DDoS analysis

Networking attacks embedded in spam emails are increasingly becoming numerous and sophisticated in nature. Hence this has given a growing need for spam email analysis to identify these attacks. The use of these intrusion detection systems has given rise to other two issues, 1) the presentation and understanding of large amounts of spam emails, 2) the user-assisted input and quantified adjustment during the analysis process. In this paper we introduce a new analytical model that uses two coefficient vectors: 'density' and 'weight'for the analysis of spam email viruses and attacks. We then use a visual clustering method to classify and display the spam emails. The visualization allows users to interactively select and scale down the scope of views for better understanding of different types of the spam email attacks. The experiment shows that this new model with the clustering visualization can be effectively used for network security analysis. © 2011 IEEE

Visual Clustering of Spam Emails for DDoS Analysis

Networking attacks embedded in spam emails are increasingly becoming numerous and sophisticated in nature. Hence this has given a growing need for spam email analysis to identify these attacks. The use of these intrusion detection systems has given rise to other two issues, 1) the presentation and understanding of large amounts of spam emails, 2) the user-assisted input and quantified adjustment during the analysis process. In this paper we introduce a new analytical model that uses two coefficient vectors: 'density' and 'weight'for the analysis of spam email viruses and attacks. We then use a visual clustering method to classify and display the spam emails. The visualization allows users to interactively select and scale down the scope of views for better understanding of different types of the spam email attacks. The experiment shows that this new model with the clustering visualization can be effectively used for network security analysis

Visualization for Biological Models, Simulation, and Ontologies

In this dissertation, I present three browsers that I have developed for the purpose of exploring, understanding, and analyzing models, simulations, and ontologies in biology and medicine. The first browser visualizes multidimensional simulation data as an animation. The second browser visualizes the equations of a complex model as a network and puts structure and organization on top of equations and variables. The third browser is an ontology viewer and editor, directly intended for the Foundational Model of Anatomy (FMA), but applicable to other ontologies as well. This browser has two contributions. First, it is a lightweight deliverable that lets someone easily dabble with the FMA. Second, it lets the user edit an ontology to create a view of it. For the ontology browser, I also conduct user studies to refine and evaluate the software

Visualize online collocation dictionary with force-directed graph

For second-language learners, collocational knowledge is very important. Knowing collocational phrases allows learners to speak and write in their targeted language naturally and reduce dramatically side effect of their first language. In order for learners to learn collocations easily, a lot of learning methods have been introduced. Particularly, learning from online-collocational corpus has become popular due to its accessibility and massive database. Although, its current presentation of information is still simple, it can be improved by using optimized representations in order to help users learning. In this thesis, we represent a suitable way to visualize online collocational dictionary by using graph representation in order to facilitate users’ learning and provide flexible exploration. Animation is also used to increase level of engagement for users. We use force-directed model for the layout, but we develop our own graph component and combine some current algorithms in order to create a proper algorithm for our purposes. The implementation is tested by a small group of participants and the results are promising

Scalability considerations for multivariate graph visualization

Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations