Abstract: The present disclosure relates to methods, systems, and computer readable media for analyzing log files for a wide variety of services (e.g., cloud computing services or microservices) to determine whether the services are operating as designed over some period of time associated with the log file(s). The present disclosure includes features and functionality for training or otherwise generating a model being configured to predict whether portions of an input log file include data reflective of normal operations of a corresponding service used to generate the input log file. The present disclosure provides a domain-agnostic approach to training an outlier detection model to analyze log files for a wide variety of services.

Abstract: The present disclosure relates to methods, systems, and computer readable media for analyzing log files for a wide variety of services (e.g., cloud computing services or microservices) to determine whether the services are operating as designed over some period of time associated with the log file(s). The present disclosure includes features and functionality for training or otherwise generating a model being configured to predict whether portions of an input log file include data reflective of normal operations of a corresponding service used to generate the input log file. The present disclosure provides a domain-agnostic approach to training an outlier detection model to analyze log files for a wide variety of services.

Abstract: A system and method for detecting anomalies in a data stream is described. The system receives the data stream that comprises values of metrics derived from observations of operation of a computing entity over a time window. A model comprising variances of the data over the time window is formed. The model identifies operating thresholds for each metric based on the variances of the data for each metric in the data stream. The system computes a steady state distance matrix of the data stream. The system determines that the steady state distance matrix exceeds a steady state threshold. In response to determining that the steady state distance matrix exceeds the steady state threshold, the system computes a pattern distance matrix based on the steady state distance matrix. The anomaly in the data stream is detected based on the pattern distance matrix. The system generates an alert indicating the anomaly.

Abstract: Methods, systems, and computer programs are presented for managing access to resources using scenarios. One method includes an operation for storing first scenario data received for a first control scenario. The first scenario data comprises a control scenario identifier, a first resource, an authorization control, and a scenario trigger that defines an event that causes an activation of the first control scenario for providing access to the first resource. Further, the method includes operations for monitoring for an occurrence of the scenario trigger and detecting an occurrence of the event associated with the scenario trigger. Further, the method determines, in response to the occurrence of the event, a set of users authorized to access the first resource, including accessing the first scenario data to determine the set of users associated with the authorization control. Furthermore, access is provided to the set of users based on the determining.

Abstract: The present disclosure relates to methods, systems, and computer readable media for analyzing log files for a wide variety of services (e.g., cloud computing services or microservices) to determine whether the services are operating as designed over some period of time associated with the log file(s). The present disclosure includes features and functionality for training or otherwise generating a model being configured to predict whether portions of an input log file include data reflective of normal operations of a corresponding service used to generate the input log file. The present disclosure provides a domain-agnostic approach to training an outlier detection model to analyze log files for a wide variety of services.

Abstract: Methods, systems, and computer programs are presented for managing access to resources using scenarios. One method includes an operation for storing first scenario data received for a first control scenario. The first scenario data comprises a control scenario identifier, a first resource, an authorization control, and a scenario trigger that defines an event that causes an activation of the first control scenario for providing access to the first resource. Further, the method includes operations for monitoring for an occurrence of the scenario trigger and detecting an occurrence of the event associated with the scenario trigger. Further, the method determines, in response to the occurrence of the event, a set of users authorized to access the first resource, including accessing the first scenario data to determine the set of users associated with the authorization control. Furthermore, access is provided to the set of users based on the determining.

Abstract: A system and method for detecting anomalies in a data stream is described. The system receives the data stream that comprises values of metrics derived from observations of operation of a computing entity over a time window. A model comprising variances of the data over the time window is formed. The model identifies operating thresholds for each metric based on the variances of the data for each metric in the data stream. The system computes a steady state distance matrix of the data stream. The system determines that the steady state distance matrix exceeds a steady state threshold. In response to determining that the steady state distance matrix exceeds the steady state threshold, the system computes a pattern distance matrix based on the steady state distance matrix. The anomaly in the data stream is detected based on the pattern distance matrix. The system generates an alert indicating the anomaly.