In some implementations, a prediction and monitoring system may processing, using a machine learning model, account data associated with an account that is associated with a user of a user device to identify a series of recurring events associated with the user device. The prediction and monitoring system may generate, using the machine learning model, a predicted transaction date and a predicted transaction amount that are both associated with the series of recurring events. The prediction and monitoring system may select, based on additional account data associated with the account and at least one of the predicted transaction date or the predicted transaction amount, a particular communication scheme, of a plurality of communication schemes, for communicating with the user. The prediction and monitoring system may transmit at least one message according to the particular communication scheme to facilitate authentication of the user.

Security can be improved in a business application or system, such as a mission-critical application, by automatically analyzing and detecting anomalies for mission-critical applications. This detection may be based on a dynamic analysis of business process logs and audit trails that includes User and Entity Behavior Analysis (“UEBA”).

Systems and methods are provided for a content-based security for computing devices. An example method includes identifying content rendered by a mobile application, the content being rendered during a session, generating feature vectors from the content and determining that the feature vectors do not match a classification model. The method also includes providing, in response to the determination that the feature vectors do not match the classification model, a challenge configured to authenticate a user of the mobile device. Another example method includes determining a computing device is located at a trusted location, capturing information from a session, the information coming from content rendered by a mobile application during the session, generating feature vectors for the session, and repeating this until a training criteria is met. The method also includes training a classification model using the feature vectors and authenticating a user of the device using the trained classification model.

A prestaging, gesture-based, access control system includes a local access assembly, a mobile device, a storage medium, and a processor. The assembly includes a controller to effect actuation between access and no-access states. The mobile device is carried by a user, and includes a detection system configured to detect a prestaging event inherently performed by the user toward an intent to gain access and followed by the detection of a primary intentional gesture specifically performed by the user toward the intent to gain access. The storage medium and the processor are configured to receive prestaging event information and primary intentional gesture information from the detection system, and execute an application to determine the performance of the prestaging event from the prestaging event information, then determine the performance of the primary intentional gesture from the primary intentional gesture information if the prestaging event is determined to have occurred.

Devices, systems and methods are disclosed for determining an electromagnetic signature for authenticating a device, a user, and/or a location. In exemplary embodiments, a magnetometer captures an electromagnetic signature which is then compared with one or more authorized electromagnetic signatures. If the electromagnetic signature matches an authorized electromagnetic signature, then access is granted. The magnetometer is integrated into a communication device having a processor and a logic. The magnetometer captures an electromagnetic signature of a surrounding environment and detects motion of the communication device through the captured electromagnetic signature. The logic on the communication device locks or unlocks features of the device based upon the captured electromagnetic signature. In further embodiments of the subject disclosure, the magnetometer is in communication with a server which authenticates a user or communication device to provide access to a remote location.

Data is received as part of an authentication procedure to identify a user. Such data characterizes a user-generated biometric sequence that is generated by the user interacting with at least one input device according to a desired biometric sequence. Thereafter, using the received data and at least one machine learning model trained using empirically derived historical data generated by a plurality of user-generated biometric sequences (e.g., historical user-generated biometric sequences according to the desired biometric sequence, etc.), the user is authenticated if an output of the at least one machine learning model is above a threshold. Data can be provided that characterizes the authenticating. Related apparatus, systems, techniques and articles are also described.

A method of a wearable device displaying icons is provided. The method includes displaying a plurality of circular icons comprising a first circular icon located in a center area of a touch display in a first size and a second circular icon located outside of the center area of the touch display in a second size smaller than the first size, and based on a direction of a touch input received on the touch display, moving the plurality of circular icons such that the first circular icon is moved to a first position located outside of the center area of the touch display and the second circular icon is moved from a second position located outside the center area of the touch display to the center area of the touch display and enlarged in size from the second size to the first size.

A method and system determines a probability that a mobile device is in use by a first user. Sensors of a mobile device are used to detect and quantify human activity and habitual or behavior traits. A collection of such habitual human trait values identifying a first user of the device are memorized during a training and learning period. During subsequent periodic predictive periods, a new collection of like habitual trait values of the current user of the device, when captured and compared with memorized values of the first user of the device relative to time, uniquely identify the person in possession of the mobile device as being or not being the first user of the device. By associating this knowledge with a unique device known to be assigned to the first user of the device, it becomes possible to confirm identity without risk of impersonation.

Methods and systems are described herein for an improved mechanism for authenticating users. In particular, the methods and systems facilitate variable authentication of users on mobile devices based on current and historical physical movement of the mobile devices at geographic locations and during predetermined time intervals while maintaining user privacy. Specifically, methods and systems authenticate users by comparing current motion data to historical motion data to determine if a user must be re-authenticated. For example, current motion data may be inconsistent with historical motion data and may cause re-authentication of a user. As another example, current motion data may be consistent with historical motion data and may not require re-authentication of a user. Moreover, the methods and systems alleviate privacy concerns by not transmitting sensitive data over one or more wired or wireless networks.

Concepts and technologies are disclosed herein for managing access based on activities of entities. A computing device can collect data that comprises an image. The computing device can identify an entity that is located in a range of a sensor. The computing device can determine an identity that is associated with the entity and an activity associated with the entity. The computing device can obtain a trust indicator associated with the entity. The computing device can determine, based on the trust indicator, if the activity should be allowed. If the computing device determines that the activity should be allowed, the computing device can initiate allowing of the activity. If the computing device determines that the activity should not be allowed, the computing device can initiate blocking of the activity.