Abstract: Incremental generation of models with dynamic clustering. A first set of data is received. A first set of clusters based on the first set of data is generated. A respective first set of models for the first set of clusters is created. A second set of data is received. A second set of clusters, based on the second set of data and based on a subset of the first set of data, is generated. A respective second set of models for the second set of clusters, based on a subset of the first set of models and based on the second set of data, is created.

Abstract: Embodiments may provide distance computations on homomorphic and/or functional encrypted vectors while detecting whether the resulting distance has wrapped around due to the vectors having elements not in an allowed range. A method of user authentication processing may comprise receiving and storing enrollment information from a client computer system, the enrollment information comprising a template of authentication data and at least one additional encrypted vector, receiving an additional template to be used to authenticate the user from the client computer system, authenticating the user using the received additional template using the stored template and the stored at least one additional encrypted vector, and determining that authentication is successful when the received additional template matches the stored template and is valid based on the stored at least one additional encrypted vector.

Abstract: An example system includes a processor to receive, at a setup or sign-up, a first cipher including a biometric template transformed using a first transformation and encrypted using a secret key, a second cipher including a security vector encrypted using the secret key, a third cipher including the biometric template transformed using a second transformation and encrypted, and a fourth cipher including an encrypted second security vector. The processor can receive, at a runtime or sign-in, a fifth cipher and a sixth cipher. The processor can verify that the fifth cipher includes a second biometric template transformed using the first transformation and encrypted using the secret key and that the sixth cipher includes the second biometric template transformed using the second transformation by testing a format attribute of the transformation functions using comparisons of inner products.

Abstract: A data manager determines an appropriate number of clusters for continuous data using unsupervised learning. The data manager selects an appropriate number of clusters based on at least one temporal stability measure between continuous data from at least two time intervals.

Abstract: Embodiments may provide techniques to detect cyber-security events in IoT data traffic that provide improved detection accuracy and preservation of privacy.

Abstract: Embodiments of the present systems and methods may provide techniques for encryption of location information, while preserving a format and semantics of the information. For example, in an embodiment, a computer-implemented method for encrypting data may comprise receiving location data and generating encrypted data from the received location data, wherein the encrypted data preserves the format and semantics of the received location data.

Abstract: A computer implemented method of authenticating a user based on comparison of biometric data authentication process parameters measured during a biometric authentication process against a biometric signature authentication process model of the user, comprising receiving sensory data captured by one or more sensors operated to capture biometric data of a user during a biometric authentication process conducted to verify a biometric signature of the user, calculating a deviation of values of a plurality of authentication process parameters measured during analysis of the sensory data from the values of corresponding reference authentication process parameters retrieved from a biometric signature authentication process model of the user and authenticating the user based on verification of the biometric signature and according to the deviation.

Abstract: Embodiments of the present invention may detect, identify, and notify of email phishing attacks. For example, a method may comprise constructing at least one behavioral model for an organization based on features extracted from a plurality of email messages and based on information relating to the organization, including analyzing behavioral patterns of emails in the organization, analyzing a plurality of new email messages using the behavioral model to determine non-binary scores representing analysis of features of the messages, including behavioral patterns of the new emails in the organization with regard to the features, determining whether any of the plurality of new email messages are malicious email messages based on the non-binary scores for the new email messages indicating that the new email messages deviate from the behavioral patterns of emails in the organization included in the behavioral model, and transmitting a notification that a message is a malicious email message.

Abstract: Embodiments of the present invention may provide the capability to identify security breaches in computer systems from clustering properties of clusters generated based on monitored behavior of users of the computer systems by using techniques that provide improved performance and reduced resource requirements. For example, behavior of users or resources may be monitored and analyzed to generate clusters and train clustering models. Labeling information relating to some user or resource may be received. When users or resources are clustered and when a cluster contains some labeled users/resources then an anomaly score can be determined for a user/resource belonging to the cluster. A user or resource may be detected to be an outlier of at least one cluster to which the user or resource has been assigned, and an alert indicating detection of the outlier may be generated.

Abstract: Setting a budget of alerts for single or multiple risk score types, adjusting a working threshold based on the set budget, wherein adjusting the working threshold is done by defining an reference threshold for an alert, providing a history of recorded risk scores within a budget sliding interval window and adjusting the working threshold such that a number of alerts which would have been provided by the recorded risk scores is calculated to stay within the set budget, and using the adjusted working threshold to normalize and optionally combine incoming risk scores so as to determine whether an incoming risk score should receive an alert.

Abstract: A computer implemented method of authenticating a user based on comparison of biometric data authentication process parameters measured during a biometric authentication process against a biometric signature authentication process model of the user, comprising receiving sensory data captured by one or more sensors operated to capture biometric data of a user during a biometric authentication process conducted to verify a biometric signature of the user, calculating a deviation of values of a plurality of authentication process parameters measured during analysis of the sensory data from the values of corresponding reference authentication process parameters retrieved from a biometric signature authentication process model of the user and authenticating the user based on verification of the biometric signature and according to the deviation.

Abstract: Embodiments of the present systems and methods may provide techniques for encryption of location information, while preserving a format and semantics of the information. For example, in an embodiment, a computer-implemented method for encrypting data may comprise receiving location data and generating encrypted data from the received location data, wherein the encrypted data preserves the format and semantics of the received location data.

Abstract: A system for detecting cyber security events can include a processor to generate a first set of a plurality of time series and aggregate statistics based on a plurality of properties corresponding to user actions for each user in a set of users. The processor can also separate the set of users into a plurality of clusters based on the first set of the plurality of time series or aggregate statistics for each user and assign an identifier to each of the plurality of clusters. Additionally, the processor can generate a second set of a plurality of time series based on properties of the plurality of clusters, wherein the properties of a cluster correspond to a membership, a diameter, and a centroid and detect an anomaly based on a new value stored in the second set of the time series. Furthermore, the processor can execute a prevention instruction.

Abstract: Embodiments of the present invention may detect, identify, and notify of email phishing attacks. For example, a method may comprise constructing at least one behavioral model for an organization based on features extracted from a plurality of email messages and based on information relating to the organization, including analyzing behavioral patterns of emails in the organization, analyzing a plurality of new email messages using the behavioral model to determine non-binary scores representing analysis of features of the messages, including behavioral patterns of the new emails in the organization with regard to the features, determining whether any of the plurality of new email messages are malicious email messages based on the non-binary scores for the new email messages indicating that the new email messages deviate from the behavioral patterns of emails in the organization included in the behavioral model, and transmitting a notification that a message is a malicious email message.

Abstract: A data manager determines an appropriate number of clusters for continuous data using unsupervised learning. The data manager selects an appropriate number of clusters based on at least one temporal stability measure between continuous data from at least two time intervals.

Abstract: A data manager determines an appropriate number of clusters for continuous data using unsupervised learning. The data manager selects an appropriate number of clusters based on at least one temporal stability measure between continuous data from at least two time intervals.

Abstract: A data manager determines an appropriate number of clusters for continuous data using unsupervised learning. The data manager selects an appropriate number of clusters based on at least one temporal stability measure between continuous data from at least two time intervals.

Abstract: Setting a budget of alerts for single or multiple risk score types, adjusting a working threshold based on the set budget, wherein adjusting the working threshold is done by defining an reference threshold for an alert, providing a history of recorded risk scores within a budget sliding interval window and adjusting the working threshold such that a number of alerts which would have been provided by the recorded risk scores is calculated to stay within the set budget, and using the adjusted working threshold to normalize and optionally combine incoming risk scores so as to determine whether an incoming risk score should receive an alert.

Abstract: A data manager determines an appropriate number of clusters for continuous data using unsupervised learning. The data manager selects an appropriate number of clusters based on at least one temporal stability measure between continuous data from at least two time intervals.

Abstract: A system for detecting cyber security events can include a processor to generate a first set of a plurality of time series and aggregate statistics based on a plurality of properties corresponding to user actions for each user in a set of users. The processor can also separate the set of users into a plurality of clusters based on the first set of the plurality of time series or aggregate statistics for each user and assign an identifier to each of the plurality of clusters. Additionally, the processor can generate a second set of a plurality of time series based on properties of the plurality of clusters, wherein the properties of a cluster correspond to a membership, a diameter, and a centroid and detect an anomaly based on a new value stored in the second set of the time series. Furthermore, the processor can execute a prevention instruction.