Visual Analytic Representation of Large Datasets f.. (VIS-SENSE)
Visual Analytic Representation of Large Datasets for Enhancing Network Security
Start date: Oct 1, 2010,
End date: Nov 30, 2013
The main goal of VIS-SENSE is the research and development of novel visual analytics technologies for the identification and prediction of very complex patterns of abnormal behavior in various application areas ranging from network information security and attack attribution to attack prediction and BGP hijacking. The ultimate goal is the enhancement of international network security so as to stimulate proactive measures that will increase the efficiency of the resolution of cyber-crime but will also enhance the prediction of such attacks. The main objective of VIS-SENSE is to research and develop novel visual analytic technologies including data analysis and mining, information visualization and user interaction methods for optimally controlling massive large amounts of data. VIS-SENSE will also provide efficient visual analytic tools for the interactive visualization of massive amounts of data, and also for the hypothesis formulation and visual validation. These tools will utilize information from all information layers, including low-level network data and results from network analytics. The goal is to provide an extensible and scalable framework for analyzing cross-layer features from the transport, service and transaction network layers. Furthermore VIS-SENSE will combine and enrich emerging attack attribution techniques with visual analytic technologies for achieving optimal performance. VIS-SENSE will deal also with attacks against the control layer of the Internet, namely the Border Gateway Control (BGP) protocol which is considered as the next natural objective for cyber-criminality. As a result, VIS-SENSE will provide a modern decision support system based on a multi-layered visual analytics framework that enables the cross-layered analysis both on the statistical correlation layer and on the interactive visualization layer.
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