Nudata Security Inc.

Mastercard Technologies Canada Ulc

Gary Wayne Richardson

Christopher Bailey

Randy Lukashuk

Christopher Everett Bailey

Systems and methods for matching and scoring sameness. In some embodiments, a computer-implemented method is provided, comprising acts of: identifying a plurality of first-degree anchor values from the first digital interaction, wherein the plurality of first-degree anchor values comprise first-degree anchor values X and Y; accessing a profile of the first-degree anchor value X, wherein: the profile of the first-degree anchor value X comprises a plurality of sets of second-degree anchor values; and each set of the plurality of sets of second-degree anchor values corresponds to a respective anchor type and comprises one or more second-degree anchor values of that anchor type; determining how closely the first-degree anchor values X and Y are associated; and generating an association score indicative of how closely the plurality of first-degree anchors are associated, based at least in part on how closely the first-degree anchor values X and Y are associated.

Systems and methods for matching and scoring sameness

Systems and methods for detecting and scoring anomalies. In some embodiments, a method is provided, comprising acts of: (A) identifying a plurality of values of an attribute, each value of the plurality of values corresponding respectively to a digital interaction of the plurality of digital interactions; (B) dividing the plurality of values into a plurality of buckets; (C) for at least one bucket of the plurality of buckets, determining a count of values from the plurality of values that fall within the at least one bucket; (D) comparing the count of values from the plurality of values that fall within the at least one bucket against historical information regarding the attribute; and (E) determining whether the attribute is anomalous based as least in part on a result of the act (D).

Systems and methods for detecting and scoring anomalies

Techniques for ascertaining legitimacy of communications received during a digital interaction with a client device. The techniques include: receiving a communication; identifying from the communication a first secured token; processing the first secured token by: obtaining, from the first secured token, information indicating a state of the digital interaction; and using the information indicating the state to determine whether the communication is from the client device; and when it is determined that the communication is from the client device, causing at least one action responsive to the communication to be performed; updating the information indicating the state of the digital interaction to obtain updated information indicating the state of the digital interaction; and providing a second secured token to the client device for use in a subsequent communication during the digital interaction, the second secured token comprising the updated information indicating the state of the digital interaction.

Systems and methods for detecting and preventing spoofing

Techniques for detecting application program spoofing. The techniques include: receiving a communication from an application program executing on a client device different from the at least one computer; identifying from the communication an asserted identity of the application program; and verifying the asserted identity of the application program at least in part by: interacting with the client device to obtain additional information about the application program, and determining whether the additional information about the application program is consistent with the asserted identity of the application program.

Techniques for detecting device type spoofing. The techniques include: receiving a communication from a client device different from the at least one computer; identifying from the communication an asserted type of the client device; and verifying the asserted type of the client device at least in part by: interacting with the client device to obtain additional information about the client device, and determining whether the additional information about the client device is consistent with the asserted type of the client device.

Systems and methods for detecting and scoring anomalies. In some embodiments, a method is provided, comprising acts of: (A) identifying a plurality of values of an attribute, each value of the plurality of values corresponding respectively to a digital interaction of the plurality of digital interactions; (B) dividing the plurality of values into a plurality of buckets; (C) for at least one bucket of the plurality of buckets, determining a count of values from the plurality of values that fall within the at least one bucket; (D) comparing the count of values from the plurality of values that fall within the at least one bucket against historical information regarding the attribute; and (E) determining whether the attribute is anomalous based at least in part on a result of the act (D).

Systems and methods for detecting and scoring anomalies. In some embodiments, a method is provided, comprising acts of: identifying a plurality of attributes from a profile; for each attribute of the plurality of attributes, determining whether the digital interaction matches the profile with respect to the attribute, comprising: identifying, from the profile, at least one bucket of possible values of the attribute, the at least one bucket being indicative of anomalous behavior; identifying, from the digital interaction, a value of the attribute; and determining whether the value identified from the digital interaction falls into the at least one bucket, wherein the digital interaction is determined to match the profile with respect to the attribute if it is determined that the value identified from the digital interaction falls into the at least one bucket; and determining a penalty score.

Systems and methods for matching and scoring sameness. In some embodiments, a computer-implemented method is provided, comprising acts of: detecting a plurality of digital interactions at different points in time; for each digital interaction of the plurality of digital interactions: identifying from the digital interaction an anchor value X of an anchor type T; and updating a profile of the anchor value X.

Systems and methods for matching and scoring sameness. In some embodiments, a computer-implemented method is provided, comprising acts of: identifying an anchor value X from the first digital interaction; identifying a first plurality of past digital interactions from which the anchor value X was previously observed, wherein each past digital interaction of the first plurality of past digital interactions has associated therewith a respective sameness score; select a second plurality of past digital interactions from the first plurality of past digital interactions based at least in part on the respective sameness scores; generating a profile for the anchor value X based on the second plurality of past digital interactions; and determining a biometric score for the first digital interaction.

Systems and methods for matching and scoring sameness. In some embodiments, a computer-implemented method is provided, comprising acts of: identifying an anchor value X from the digital interaction; identifying, in a record of the digital interaction, a data structure associated with an anchor type T of the anchor value, wherein a plurality of anchor values of the anchor type T are divided into a plurality of buckets of anchor values; identifying a bucket B of the plurality of buckets of anchor values, wherein the anchor value X falls into the bucket B; and operating on the data structure associated with the anchor type T to indicate that at least one anchor value from the bucket B has been observed in connection with the digital interaction.

Systems and methods for detecting and scoring anomalies. In some embodiments, a method is provided, comprising acts of: determining whether the digital interaction is suspicious; in response to determining that the digital interaction is suspicious, deploying a security probe of a first type to collect first data from the digital interaction; analyzing first data collected from the digital interaction by the security probe of the first type to determine if the digital interaction continues to appear suspicious; and if the first data collected from the digital interaction by the security probe of the first type indicates that the digital interaction continues to appear suspicious, deploying a security probe of a second type to collect second data from the digital interaction.

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Port Moody, Canada

Gary Wayne Richardson