Man in the middle attack detection using active learning
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United States of America Patent
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Oct 3, 2017
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N/A
app pub date -
Sep 30, 2016
filing date -
Sep 30, 2016
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Abstract
Data is received that includes a plurality of samples that each characterize interception of data traffic to a computing device over a network. Thereafter, the plurality of samples characterizing the interception of data traffic are grouped into a plurality of clusters. At least a portion of the samples are labeled to characterize a likelihood of each such sample as relating to an unauthorized interception of data traffic. Each cluster is assigned with a label corresponding to a majority of samples within such cluster. At least one machine learning model is trained using the assigned labeled clusters such that, once trained, the at least one machine learning model determines a likelihood of future samples as relating to an unauthorized interception of data traffic to a corresponding computing device.
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