Method and system for detecting abnormal online user activity
First Claim
1. A method for detecting abnormal online user activities, the method being implemented on a computer comprising at least one processor, storage, and communications circuitry, the method comprising:
- obtaining, by the at least one processor, baseline distribution data representing a baseline distribution characterizing normal user activities with respect to a first entity;
receiving, dynamically, first information related to online user activities with respect to the first entity;
determining first distribution data representing a first dynamic distribution based, at least in part, on the first information;
computing, using the baseline distribution data and the first distribution data, at least one measure characterizing a difference between the baseline distribution and the first dynamic distribution;
assessing in real-time whether the first information indicates abnormal user activity behavior based, at least in part, on the at least one measure, wherein the abnormal user activity behavior is identified by detecting time-to-click (“
TTC”
) abnormalities, which includes a duration between when an advertisement is rendered and when a user clicks on the advertisement, and wherein the abnormal user activity behavior signifies fraudulent activities by at least one of a bot or fake user clicks; and
generating, in response to determining that the first information indicates that first distribution data comprises a first indication of the abnormal user activity behavior, first output data comprising at least the first distribution data and the at least one measure.
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Accused Products
Abstract
The present teaching generally relates to detecting abnormal user activity associated with an entity. In a non-limiting embodiment, baseline distribution data representing a baseline distribution characterizing normal user activities for an entity may be obtained. Information related to online user activities with respect to the entity may be received, distribution data representation a dynamic distribution may be determined based, at least in part, on the information. One or more measures characterizing a difference between the baseline distribution and the dynamic distribution may be computed, and in real-time it may be assessed whether the information indicates abnormal user activity. If the first information indicates abnormal user activity, then output data including the distribution data and the one or more measures may be generated.
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Citations
20 Claims
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1. A method for detecting abnormal online user activities, the method being implemented on a computer comprising at least one processor, storage, and communications circuitry, the method comprising:
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obtaining, by the at least one processor, baseline distribution data representing a baseline distribution characterizing normal user activities with respect to a first entity; receiving, dynamically, first information related to online user activities with respect to the first entity; determining first distribution data representing a first dynamic distribution based, at least in part, on the first information; computing, using the baseline distribution data and the first distribution data, at least one measure characterizing a difference between the baseline distribution and the first dynamic distribution; assessing in real-time whether the first information indicates abnormal user activity behavior based, at least in part, on the at least one measure, wherein the abnormal user activity behavior is identified by detecting time-to-click (“
TTC”
) abnormalities, which includes a duration between when an advertisement is rendered and when a user clicks on the advertisement, and wherein the abnormal user activity behavior signifies fraudulent activities by at least one of a bot or fake user clicks; andgenerating, in response to determining that the first information indicates that first distribution data comprises a first indication of the abnormal user activity behavior, first output data comprising at least the first distribution data and the at least one measure. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system for detecting abnormal online user activities, the system comprising:
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a baseline distribution generation system configured to obtain baseline distribution data representing a baseline distribution characterizing normal user activities with respect to a first entity; an online data collection system configured to receive, dynamically, first information related to online user activities with respect to the first entity; an online data distribution generation system configured to determine first distribution data representing a first dynamic distribution based, at least in part, on the first information; a distribution measure system configured to compute, using the baseline distribution data and the first distribution data, at least one measure characterizing a difference between the baseline distribution and the first dynamic distribution; an abnormal user activity detection system configured to assess in real-time whether the first information indicates abnormal user activity behavior based, at least in part, on the at least one measure, wherein the abnormal user activity behavior is identified by detecting time-to-click (“
TTC”
) abnormalities, which includes a duration between when an advertisement is rendered and when a user clicks on the advertisement, and wherein the abnormal user activity behavior signifies fraudulent activities by at least one of a bot or fake user clicks; andan output data generation system configured to generate, in response to determining that the first information indicates that first distribution data comprises a first indication of the abnormal user activity behavior, first output data comprising at least the first distribution data and the at least one measure. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A non-transitory computer readable medium having information recorded thereon for detecting abnormal online user activity, wherein the information, when read by the computer, causes the computer to:
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obtain baseline distribution data representing a baseline distribution characterizing normal user activities with respect to a first entity; receive, dynamically, first information related to online user activities with respect to the first entity; determine first distribution data representing a first dynamic distribution based, at least in part, on the first information; compute, using the baseline distribution data and the first distribution data, at least one measure characterizing a difference between the baseline distribution and the first dynamic distribution; assess in real-time whether the first information indicates abnormal user activity behavior based, at least in part, on the at least one measure, wherein the abnormal user activity behavior is identified by detecting time-to-click (“
TTC”
) abnormalities, which includes a duration between when an advertisement is rendered and when a user clicks on the advertisement, and wherein the abnormal user activity behavior signifies fraudulent activities by at least one of a bot or fake user clicks; andgenerate, in response to determining that the first information indicates that first distribution data comprises a first indication of the abnormal user activity behavior, first output data comprising at least the first distribution data and the at least one measure.
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Specification