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Visual Correlation for Situational Awareness
Yarden Livnat, Jim Agutter, Shaun Moon, Stefano Foresti
University of Utah
Project Description:
Presented at the Infovis 2005 Conference in Minneapolis, MN (USA), VisAware reveals a novel visual correlation paradigm that takes advantage of human perceptive and cognitive facilities in order to enhance users' situational awareness and support decision-making.

The first image reflects VisAware used in a Biowatch scenario where its structure classifies agents in colored sections around a ring. It shows the different categories of biological agents and the different types of chemical agents (i.e. blistering and nerve agents). With the map in the middle, it is easy to correlate the presence of agents to the sensor that detected it. The correlating line has a variable width that shows the probability of the agent under analysis; the thicker the line the greater the probability of an actual attack.

The second image shows VisAlert, a visualization method for network intrusion detection. The authors based their approach on representing the network alerts as connections between two domains. These two domains are a one dimensional domain representing the node attribute, and a two-dimensional domain representing the time and type attributes. A network alert instance, in this scheme, is thus a straight line from a point in the type-time domain to a point in the node domain. They choose to separate the node attribute from the type and time as nodes provide a more or less static set of objects that can be used as visualization anchors for the transient alert instances.

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