Identifying 802.11 Traffic from Passive Measurements Using Iterative Bayesian Inference
by Wei Wei, Sharad Jaiswal, Jim Kurose, Don Towsley
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| url: | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4146944 | | abstract: | In this paper, we propose a classification scheme
to differentiate Ethernet and WLAN TCP flows based on measurements
collected passively at the edge of a large network.
This classifier computes the fraction of wireless TCP flows,
and the degree of belief that a TCP flow traverses a WLAN
inside the network. The core of this classifier is an iterative
Bayesian inference algorithm that we developed to obtain the
maximum likelihood estimate (MLE) of these quantities. Our
algorithm can handle any general two-class classification problem
given the marginal distributions of these two classes. Numerical
and experimental evaluations demonstrate that our classifier
obtains accurate results and is insensitive to imprecise marginal
distributions. We apply the classifier to various traces collected
at the edge of the UMass campus network and infer that between
11-14% of all TCP flows coming into UMass campus traverse an
802.11 wireless link within the campus. We also detect wireless
usage (through the use of private routers and access points) in
areas not covered by the official wireless infrastructure. |
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