Oath Inc.

Verizon Media Inc.

Yahoo Ad Tech LLC

Chun Han

Liang Wang

Angus Xianen Qiu

Liang Peng

Ruichen Wang

Yan Han

Teppo Veikko Salonen

Xianen Qiu

Jaime Morales, Jr

The present teaching relates to a fraud detecting system and method for providing protection against fraudulent advertisement requests. Upon receiving a request for an advertisement, the system extracts an identifier, associated with a source from which the request originates, included in the request. The system determines whether the extracted identifier is included in a list of designated identifiers, and when the identifier is included in the list, the system denies the request for the advertisement. When the identifier is not included in the list of designated identifiers, the system provides the advertisement in response to the request, and extracts a set of features from the request and other requests that originate from the source to determine whether the identifier associated with the source is to be included in the list of designated identifiers based on the set of features in accordance with one or more models.

Method and system for detecting fraudulent advertisement activity

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.

Method and system for detecting abnormal online user activity

The present teaching generally relates to detecting fraudulent networks. First data associated with a plurality of entities may be obtained, and a representation characterizing similarities among the plurality may be generated. Based on the representation, at least one entity cluster may be identified as corresponding to a candidate fraud network. A score associated with each of the at least one entity cluster may be determined, where the score indicates a likelihood that a corresponding entity cluster represents a fraud network, and at least some of the at least one entity cluster may be identified as a fraud network based on the score.

Method and system for identifying fraudulent publisher networks

Services often utilize scoring techniques to distinguish between user requests that comply with a usage policy of the service from those that represent a misuse of the service. Users who endeavor to misuse the service engages in probing by submitting a variety of requests to the service until one such request exhibiting a score that is within a score threshold, and then patterning further requests that misuse the service upon the successful request. Instead, when a first request from a user is identified that violates the score threshold, a score offset is selected. The scores of second and future requests by the user are adjusted by the score offset, indicating increased suspicion of the user's requests, while allowing legitimate requests that fulfill the score threshold by a larger margin. Additionally, absent further misuse, the score offset decays over time to restore trust incrementally in the legitimacy of the user's requests.

Adaptive scoring of service requests and determining whether to fulfill service requests

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Fremont, CA, United States of America

Chun Han