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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Interactive Visualization of Network Anomalous Events
verfasst von : Yang Cai, Rafael de M. Franco
Erschienen in: Computational Science – ICCS 2009
Verlag: Springer Berlin Heidelberg
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We present an interactive visualization and clustering algorithm that reveals real-time network anomalous events. In the model, glyphs are defined with multiple network attributes and clustered with a recursive optimization algorithm for dimensional reduction. The user’s visual latency time is incorporated into the recursive process so that it updates the display and the optimization model according to a human-based delay factor and maximizes the capacity of real-time computation. The interactive search interface is developed to enable the display of similar data points according to the degree of their similarity of attributes. Finally, typical network anomalous events are analyzed and visualized such as password guessing, etc. This technology is expected to have an impact on visual real-time data mining for network security, sensor networks and many other multivariable real-time monitoring systems.