Atypical behavior identification in large-scale network traffic

Cyber analysts are faced with the daunting challenge of identifying exploits and threats within potentially billions of daily records of network traffic. Enterprise-wide cyber traffic involves hundreds of millions of distinct IP addresses and results in data sets ranging from terabytes to petabytes...

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Vydané v:2011 IEEE Symposium on Large Data Analysis and Visualization s. 15 - 22
Hlavní autori: Best, D. M., Hafen, R. P., Olsen, B. K., Pike, W. A.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.10.2011
Predmet:
Analytical models
cyber analytics
Data models
Data visualization
IP networks
large-scale data
Measurement
Time series
Visual analytics
ISBN:9781467301565, 1467301566
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Popis
Shrnutí:Cyber analysts are faced with the daunting challenge of identifying exploits and threats within potentially billions of daily records of network traffic. Enterprise-wide cyber traffic involves hundreds of millions of distinct IP addresses and results in data sets ranging from terabytes to petabytes of raw data. Creating behavioral models and identifying trends based on those models requires data intensive architectures and techniques that can scale as data volume increases. Analysts need scalable visualization methods that foster interactive exploration of data and enable identification of behavioral anomalies. Developers must carefully consider application design, storage, processing, and display to provide usability and interactivity with large-scale data. We present an application that highlights atypical behavior in enterprise network flow records. This is accomplished by utilizing data intensive architectures to store the data, aggregation techniques to optimize data access, statistical techniques to characterize behavior, and a visual analytic environment to render the behavioral trends, highlight atypical activity, and allow for exploration.
ISBN:9781467301565
1467301566
DOI:10.1109/LDAV.2011.6092312