公开(公告)号：US10397256B2

公开(公告)日：2019-08-27

申请号：US15365008

申请日：2016-11-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ori Kashi ,  Philip Newman ,  Daniel Alon ,  Elad Yom-Tov ,  Hani Neuvirth ,  Royi Ronen

IPC: H04L29/06 ,  G06N20/00 ,  G06N7/00 ,  H04L12/58 ,  H04L29/08

Abstract: In an example embodiment, a computer-implemented method comprises obtaining labels from messages associated with an email service provider, wherein the labels indicate for each message IP how many spam and non-spam messages have been received; obtaining network data features from a cloud service provider; providing the labels and network data features to a machine learning application; generating a prediction model representing an algorithm for determining whether a particular set of network data features are spam or not; applying the prediction model to network data features for an unlabeled message; and generating an output of the prediction model indicating a likelihood that the unlabeled message is spam.

公开(公告)号：US20170359362A1

公开(公告)日：2017-12-14

申请号：US15365008

申请日：2016-11-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ori Kashi ,  Philip Newman ,  Daniel Alon ,  Elad Yom-Tov ,  Hani Neuvirth ,  Royi Ronen

IPC: H04L29/06 ,  H04L29/08 ,  G06N99/00 ,  G06N7/00 ,  H04L12/58

CPC classification number: H04L63/1425 ,  G06N7/005 ,  G06N20/00 ,  H04L51/12 ,  H04L51/16 ,  H04L67/10

Abstract: In an example embodiment, a computer-implemented method comprises obtaining labels from messages associated with an email service provider, wherein the labels indicate for each message IP how many spam and non-spam messages have been received; obtaining network data features from a cloud service provider; providing the labels and network data features to a machine learning application; generating a prediction model representing an algorithm for determining whether a particular set of network data features are spam or not; applying the prediction model to network data features for an unlabeled message; and generating an output of the prediction model indicating a likelihood that the unlabeled message is spam.