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公开(公告)号：US10129295B2
公开(公告)日：2018-11-13
申请号：US15253586
申请日：2016-08-31
Applicant: Microsoft Technology Licensing, LLC
Inventor： Omer Karin , Royi Ronen , Hani Neuvirth , Roey Vilnai
Abstract: Use machine learning to train a classifier to classify entities to increase confidence with respect to an entity being part of a distributed denial of service attack. The method includes training a classifier to use a first classification method, to identify probabilities that entities from a set of entities are performing denial of service attacks. The method further includes identifying a subset of entities meeting a threshold probability of performing a denial of service attack. The method further includes using a second classification method, identifying similarity of entities in the subset of entities. The method further includes based on the similarity, classifying individual entities.
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公开(公告)号：US20180063188A1
公开(公告)日：2018-03-01
申请号：US15253586
申请日：2016-08-31
Applicant: Microsoft Technology Licensing, LLC
Inventor： Omer Karin , Royi Ronen , Hani Neuvirth , Roey Vilnai
CPC classification number: H04L63/1458 , G06F17/30598 , G06F17/30705 , G06N99/005 , H04L63/1425 , H04L63/1466 , H04L2463/144
Abstract: Use machine learning to train a classifier to classify entities to increase confidence with respect to an entity being part of a distributed denial of service attack. The method includes training a classifier to use a first classification method, to identify probabilities that entities from a set of entities are performing denial of service attacks. The method further includes identifying a subset of entities meeting a threshold probability of performing a denial of service attack. The method further includes using a second classification method, identifying similarity of entities in the subset of entities. The method further includes based on the similarity, classifying individual entities.
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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
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.
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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
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.
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