This paper describes Isis, a system that uses progressive multiples of timelines and event plots to support the iterative investigation of intrusions by experienced analysts using network flow data. The visual representations have been designed to make temporal relationships apparent, allow visual classification of events with dynamic brushing, and enable users to organize their visualizations to reveal traffic structure and patterns by reordering rows. Isis combines visual affordances with SQL to provide a flexible tool for investigation. We present an annotated case study using anonymized data of a real intrusion that demonstrates the features of Isis.

10.1007/978-3-540-78243-8_6