{"id":{"repo_id":"uno","oai_identifier":"oai:scholarworks.uno.edu:td-2320"},"canonical_url":"https://search.dev.ndltd.org/etd/uno/oai:scholarworks.uno.edu:td-2320","repository":{"repo_id":"uno","name":"University of New Orleans","base_url":"https://scholarworks.uno.edu/do/oai/"},"display":{"title":"Actionable Visualization of Higher Dimensional Dynamical Processes","abstract":"Analyzing modern day's information systems that produce humongous multi-dimensional data in form of logs, traces or events that unfold over time can be tedious without adequate visualization, thereby, advocating the need for an intelligible visualization. This thesis researched and developed a visualization framework that represents multi-dimensional dynamic and temporal process data in a potentially intelligible and actionable form. A prototype showing four different views using notional malware data abstracted from Normal Sandbox behavioral traces were developed. 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This thesis researched and developed a visualization framework that represents multi-dimensional dynamic and temporal process data in a potentially intelligible and actionable form. A prototype showing four different views using notional malware data abstracted from Normal Sandbox behavioral traces were developed. In particular, the B-matrix view representing the DLL files used by the malware to attack a system. 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