SDM 2014
Conference paper

Kaleido: Network traffic attribution using multifaceted footprinting

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Network traffic attribution, namely, inferring users responsible for activities observed on network interfaces, is one fundamental yet challenging task in network security forensics. Compared with other user-system interaction records, network traces are inherently coarsegrained, context-sensitive, and detached from user ends. This paper presents Kaleido, a new network traffic attribution tool with a series of key features: a) it adopts a new class of inductive discriminant models to capture user- and context-specific patterns ("footprints") from different aspects of network traffic; b) it applies efficient learning methods to extracting and aggregating such footprints from noisy historical traces; c) with the help of novel indexing structures, it is able to perform efficient, runtime traffic attribution over high-volume network traces. The efficacy of Kaleido is evaluated with extensive experimental studies using the real network traces collected over three months in a large enterprise network.


24 Apr 2014


SDM 2014