Abstract: Methods and systems for unique session number sharing to ensure traceability are discussed herein. According to an implementation, a user sends a request to login a browser from a user equipment to a server device. The server device validates a user credential associated with the browser by comparing the user credential with pre-stored user registration information. Once the user credential is validated, the server device generates a session with a unique session number (USN) with respect to the request. The server device generates a plurality of logs with respect to the activities occurred during the session and associates the USN with each of the multiple logs. The USN is further included in an access token that authorizes the user to access the websites hosted by the browser.

Abstract: Described herein are techniques, devices, and systems for utilizing account number pins to generate enhanced tokens for user authentication. Tokens may be enhanced by a telecommunication service provider in response to the account number pins being received and validated. The enhanced tokens may be utilized for different types of processes associated with requests received from user equipment, customer care devices, and/or retail devices.

Abstract: A network-accessible service such as a web site may authenticate users through a login process. In order to detect possibly fraudulent login events, the service may implement a framework based on recorded login events. For example, attributes of multiple recorded login events may be analyzed to create a framework that can be applied to attributes of newly received login requests to predict whether the newly received login requests are fraudulent. The framework may comprise criteria, algorithms, rules, models, and/or techniques, and may be constructed using various means such as pattern recognition, machine learning, and/or cluster analysis.

Abstract: A network-accessible service such as a web site may authenticate users through a login process. In order to detect possibly fraudulent login events, the service may implement a framework based on recorded login events. For example, attributes of multiple recorded login events may be analyzed to create a framework that can be applied to attributes of newly received login requests to predict whether the newly received login requests are fraudulent. The framework may comprise criteria, algorithms, rules, models, and/or techniques, and may be constructed using various means such as pattern recognition, machine learning, and/or cluster analysis.

Abstract: A network-accessible service such as a web site may authenticate users through a login process. In order to detect possibly fraudulent login events, the service may implement a framework based on recorded login events. For example, attributes of multiple recorded login events may be analyzed to create a framework that can be applied to attributes of newly received login requests to predict whether the newly received login requests are fraudulent. The framework may comprise criteria, algorithms, rules, models, and/or techniques, and may be constructed using various means such as pattern recognition, machine learning, and/or cluster analysis.

Abstract: Systems and methods for biometric subscriber account authentication are described. When a subscriber initially accesses subscriber account data maintained by the communication network, a first non-biometric authentication protocol is used. The subscriber may then be invited to set up biometric authentication, which includes generating a private key/public key pair. Subsequently, biometric authentication is performed at the user device to unlock the private key/public key pair, which is then used to support authentication of the subscriber to the communication network using a second authentication protocol.

Abstract: A network-accessible service such as a web site may authenticate users through a login process. In order to detect possibly fraudulent login events, the service may implement a framework based on recorded login events. For example, attributes of multiple recorded login events may be analyzed to create a framework that can be applied to attributes of newly received login requests to predict whether the newly received login requests are fraudulent. The framework may comprise criteria, algorithms, rules, models, and/or techniques, and may be constructed using various means such as pattern recognition, machine learning, and/or cluster analysis.