Preliminary Steps in Sonifying Web Log Data
Author(s)
Hall, David L.
Gourley, Matthew
Panulla, Brian
Ballora, Mark
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Abstract
Detection of intrusions is a continuing problem in network
security. Due to the large volumes of data recorded in Web
server logs, analysis is typically forensic, taking place only after
a problem has occurred. We are exploring the detection of
intrusion signatures and patterns via an auditory display. Web
log data is parsed and formatted using Python, then read as a
data array by the synthesis language SuperCollider, which
renders it as a sonification. This can be done either for the study
of pre-existing data sets or in monitoring Web traffic in real
time. Components rendered aurally include IP address,
geographical information, and server Return Codes. Users can
interact with the data, speeding or slowing the speed of
representation (for pre-existing data sets) or “mixing” sound
components to optimize intelligibility for tracking suspicious
activity.
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Date
2010-06
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Text
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Proceedings