Title
Imaging and visual analysis - Detecting distributed scans using high-performance query-driven visualization
Abstract
Modern forensic analytics applications, like network traffic analysis, perform high-performance hypothesis testing, knowledge discovery and data mining on very large datasets. One essential strategy to reduce the time required for these operations is to select only the most relevant data records for a given computation. In this paper, we present a set of parallel algorithms that demonstrate how an efficient selection mechanism -- bitmap indexing -- significantly speeds up a common analysis task, namely, computing conditional histogram on very large datasets. We present a thorough study of the performance characteristics of the parallel conditional histogram algorithms. As a case study, we compute conditional histograms for detecting distributed scans hidden in a dataset consisting of approximately 2.5 billion network connection records. We show that these conditional histograms can be computed on interactive time scale (i.e., in seconds). We also show how to progressively modify the selection criteria to narrow the analysis and find the sources of the distributed scans.
Year
DOI
Venue
2006
10.1145/1188455.1188542
SC
Keywords
DocType
Citations 
data mining,visual analytics,network security,query-driven visualization,network connection analysis,visual analysis,parallel algorithm,indexation,hypothesis test
Conference
2
PageRank 
References 
Authors
0.43
18
5
Name
Order
Citations
PageRank
Kurt Stockinger11471127.33
E. Wes Bethel243839.76
Scott Campbell3132.04
Eli Dart4566.44
Kesheng Wu51231108.30