Abstract: A system for detecting whether a device seeking communication with a server is a returning device that previously communicated with the server includes a database that stores groups of device attributes based on observable device characteristics and unique identifiers. The database is generally not accessible to the devices. Each attribute group and the associated device identifier (DID) can uniquely identify a particular device, and the associated DID is generally not derivable from the attributes. The database may satisfy a uniqueness property so that each attribute value in the database may also uniquely identify a device.

Abstract: A system for establishing a trusted path for secure communication between client devices and server devices, such as between an account holder and a financial institution, can provide the core security attributes of confidentiality (of the parties), integrity (of the information), anti-replay (protection against replay fraud) and/or anti-tampering (protection against unauthorized changes to information being exchanged and/or modules that generate and communicate such information). A messaging layer implementation in favor of a transport layer implementation can provide a trusted path. This infrastructure features secure cryptographic key storage, and implementation of a trusted path built using the cryptographic infrastructure. The trusted path protects against unauthorized information disclosure, modification, or replays. These services can effectively protect against Man-in-the-Middle, Man-in-the-Application, and other attacks.

Abstract: A system for detecting whether a device seeking communication with a server is a returning device that previously communicated with the server includes a database that stores groups of device attributes based on observable device characteristics and unique identifiers. The database is generally not accessible to the devices. Each attribute group and the associated device identifier (DID) can uniquely identify a particular device, and the associated DID is generally not derivable from the attributes. The database may satisfy a uniqueness property so that each attribute value in the database may also uniquely identify a device.

Abstract: A system of classifying devices and/or app instances a new or returning divides attributes generated from observations received from an uncharacterized device/software app into base-fingerprint attributes and predictor attributes, where the two kinds of attributes have different longevities. Predictor attribute tuples from attribute tuples having the same base fingerprint as the base fingerprint corresponding to the uncharacterized device/app, and the predictor attribute tuple corresponding to the uncharacterized device/app are analyzed using a machine learned predictor function to obtain a final fingerprint. Machine learning techniques such as logistic regression, support vector machine, and artificial neural network can provide a predictor function that can decrease the conflict rate of the final fingerprint and, hence, the utility thereof, without significantly affecting the accuracy of classification.

Abstract: A system for establishing a trusted path for secure communication between client devices and server devices, such as between an account holder and a financial institution, can provide the core security attributes of confidentiality (of the parties), integrity (of the information), anti-replay (protection against replay fraud) and/or anti-tampering (protection against unauthorized changes to information being exchanged and/or modules that generate and communicate such information). A messaging layer implementation in favor of a transport layer implementation can provide a trusted path. This infrastructure features secure cryptographic key storage, and implementation of a trusted path built using the cryptographic infrastructure. The trusted path protects against unauthorized information disclosure, modification, or replays. These services can effectively protect against Man-in-the-Middle, Man-in-the-Application, and other attacks.

Abstract: A system of classifying devices and/or app instances a new or returning divides attributes generated from observations received from an uncharacterized device/software app into base-fingerprint attributes and predictor attributes, where the two kinds of attributes have different longevities. Predictor attribute tuples from attribute tuples having the same base fingerprint as the base fingerprint corresponding to the uncharacterized device/app, and the predictor attribute tuple corresponding to the uncharacterized device/app are analyzed using a machine learned predictor function to obtain a final fingerprint. Machine learning techniques such as logistic regression, support vector machine, and artificial neural network can provide a predictor function that can decrease the conflict rate of the final fingerprint and, hence, the utility thereof, without significantly affecting the accuracy of classification.