Lexisnexis Risk Solutions Fl Inc.

Philip Lindblad

Per Burström

Tony Libell

Mikael Åhlén

Matthias Baumhof

Julian Breitling

Lancelot Pregniard

Ingo Deutschmann

Systems and methods are provided for behavioral biometrics retraining on credential input provided by a user for authentication. A method includes receiving a successful login indication that a user has been authenticated for access on the enterprise server based on the credential input. The method includes receiving metadata and user behaviometric data corresponding to the credential input. The method includes computing, with a behavioral scoring module, a metadata similarity score by comparing the received metadata to metadata previously stored in a user profile, resetting at least a portion of previously stored user profile data based on receiving the successful login indication and the metadata similarity score being less than a threshold value, and training user profile data using the received user behaviometric data based on receiving the successful login indication and the metadata similarity score being less than a threshold value.

Systems and methods for faster behavioral retraining

System and methods are provided for mitigating click farm fraud by receiving network data and sensor data from a plurality of computing devices, extracting one or more features from the sensor data and the network data for each of the devices. The features represent one or more of a local physical environment and communication channel environment associated with a device of the plurality of computing devices. The method includes determining one or more subsets of the plurality of computing devices based on environmental and network characteristics of the one or more features, identifying, based on the one or more subsets and detected influencer activities, co-located computing devices, and responsive to determining that a count of the co-located computing devices is greater than a predetermined count, sending a session terminating command to one or more servers in communication with the co-located computing devices to mitigate click farm fraudulent activities.

Systems and methods for detecting and mitigating click farm fraud

The systems and methods are provided that can enable the detection of certain modes of online interactions carried out by a user's computing device, for example, when an online app or webpage of an enterprise is accessed by the user's computing device. Certain exemplary implementations may utilize collector code that resides in the app or webpage opened by users accessing the enterprise service to measure and collect timing data to detect whether the user's computing device or associated browsing session is subjected to modes of manipulation such as the user browser's privacy mode being engaged, malware interacting with the browsing session, and/or some type of aggregator interacting with the browsing session. Such modes of manipulation can impact the utility and accuracy of certain forms of behavioral biometric algorithms, particularly those that utilize users' typing, timing, keystroke dwell, etc.

Systems and methods for detecting browser mode

Systems and methods are provided for improving behavioral biometrics scoring accuracy. A method includes receiving streamed behavioral biometrics data and associated contextual data, determining a first trust score by evaluating the streamed behavioral biometric data, and if the first trust score is below a first predetermined value, separating the streamed behavioral biometrics data into contextualized bins corresponding to the contextual data to produce binned data. The contextualized bins can include a target identifier such as a data field; or a data type field, an area of a browser window, a task, and/or a physical activity. Responsive to a first threshold amount of accrued binned data, the method can include evaluating the binned data to determine a second trust score that is context-aware and sending an alert to the enterprise server responsive to the second trust score being below a second predetermined value.

Systems and methods for improved behavioral scoring accuracy

Systems and methods are provided for detecting when a user uses one or both hands to interact with a device having a physical or virtual keyboard. In one implementation, one-handed typing is determined by analyzing intragroup and intergroup keyflight timing distributions between different groups of clustered keys, each cluster having distinctly different proximal properties. A statistical measure made between the intragroup and intergroup keyflight timing distributions may be performed to determine one-handed or two-handed typing input, which may be used to enhance a user experience by adapting a viewing or input element to an appropriate hand setting. In another implementation, the detection may be used as input to determine a likelihood of suspected coaching fraud for online banking applications. In yet another implementation, the detection of one-handed typing is used as an input to bot detection algorithms to reduce false positives from other modalities.

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Lidingö, Sweden

Philip Lindblad