A method may include obtaining a request to unblock a predetermined website in a network and that is associated with a predetermined list. The predetermined list may be used to determine whether a respective user device among various user devices can access one or more websites. The method may further include determining an impact level of the predetermined website for an organization using a machine-learning algorithm and website gateway data. The method may further include determining a probability of a security breach using the machine-learning algorithm and threat data. The method may further include determining whether to unblock the predetermined website based on the impact level and the probability of a security breach. The method may further include transmitting, in response to determining that the predetermined website should be unblocked, a command that modifies the predetermined list to enable the respective user device to access the predetermined website.

Methods, systems and apparatus for assessing the likely status of an operator of a computing device interacting with a server as a human operator or an autonomic computer application, such as a “bot” are described herein. By monitoring at least some data, e.g., biometric data, generated at the client computing device, a comparison can be made between the monitored data and model data relating to human interaction with the computing device. The results of the comparison can lead to a value that represents the likelihood that the monitored data results from human interaction.

There is provided, in accordance with some embodiments, a method comprising using one or more hardware processors for receiving a behavioral biometric model that characterizes a human user according to pointing device data of the human user, where the pointing device data comprises screen coordinate and time stamp pairs. The method comprises an action of monitoring an input data stream from a pointing device in real time, wherein the input data stream covers two or more spatial regions of a display screen, and an action of segregating the input data stream into one or more subset streams that is restricted to one of the plurality of spatial regions. The method comprises an action of computing a similarity score based on one or more comparisons of the behavioral biometric model and the one or more subset streams, and an action of sending the similarity score to a user authorization system.

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, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

An improved authentication, identification, and/or verification system is provided in various embodiments. The system is provided for use in relation to provisioning access or establishing identity in relation to one or more human users, and may be used in a single site/scenario/system, or across multiple sites/scenarios/systems. A combination of biometric modalities and authentication mechanisms having diverse characteristics are utilized to establish identity, the diverse characteristics being utilized to modify aspects of identity management and access provisioning.

According to the present disclosure, a handwritten signature to be authenticated is received, a plurality of pieces of signature behavioral characteristic information are extracted, all of the plurality of the pieces of the extracted signature behavioral characteristic information are applied to each of first and second signature authentication algorithms using different techniques to analyze a degree of matching between the received handwritten signature and a registered handwritten signature, results of analysis performed by the first and second signature authentication algorithms are combined to adjust a false rejection rate and a false acceptance rate, and whether handwritten signature authentication succeeds is finally determined.

An automated teller machine (ATM) may include an input component and one or more processors. The input component may be configured to detect multi-factor input associated with an account. The multi-factor input may include at least two of: a sequence of characters input via the input component, a force with which at least one character, of the sequence of characters, is input via the input component, a length of time over which at least one character, of the sequence of characters, is input via the input component, or a combination of at least two characters, of the sequence of characters, that are input concurrently via the input component. The ATM may receive the multi-factor input, validate the multi-factor input in association with the account, and selectively permit or deny access to one or more actions associated with the account based on validating the multi-factor input.

A management apparatus includes a memory, a unification policy setting unit, and a security level setting unit. The memory stores, for each of a user belonging to a first group and a user belonging to a second group, an authentication level of a domain assigned to a corresponding one of the users. The unification policy setting unit sets a unification policy that specifies a relationship between the authentication level and a security level for a state after unification. The security level setting unit sets the security level in a case where the first group and the second group undergo the unification into a third group. The security level is set for each of the users belonging to the third group by using the authentication level and the unification policy.

Account permissions and data accessibility can be modified based on level of confidence for a login attempt to the account. User activity observations corresponding to one or more login attempts to access a user account can be stored. A confidence score associated with a successful login attempt of the user account can be determined. The confidence score is based on the user activity observations. A level of access to an application with functions and data for the user account can be determined. The level of access is based on the confidence score. The level of access is associated with the functions and the data that are executable and accessible subsequent to the successful login attempt.