Abstract: A method includes defining a set of context types; defining a set of source types, each comprising context types; defining, for each source type, and for each context type included in the events from data sources having the source type, a context definition comprising a set of fields, in events from the data sources, that are associated with the context type; receiving a query comprising a first field value and a time period; retrieving a plurality of events that include the first field value and the time period; for each retrieved event, and for each context definition defined for a source type and a context type of a data source from which the retrieved event originated, determining field values of fields in the set of fields of the context definition; aggregating, for each context type, determined field values from the events; and generating an output.

Abstract: Querying of time-aware metrics time series includes receiving a query, the query comprising a set of query metadata and a query time range. It further includes, based at least in part on the set of query metadata and the query time range, selecting a time series from a plurality of metrics time series. Each metrics time series in the plurality of metrics time series is associated with a set of metadata and an active interval of time. A set of metadata associated with the selected time series matches the set of query metadata, and an active interval of time associated with the selected metrics time series intersects with the query time range. The selected metrics time series is returned.

Abstract: Key name synthesis is disclosed. A metrics data point is received. Based at least in part on a translation statement, at least a portion of the received metrics data point is associated with a key specified by the translation statement such that the specified key and the associated at least portion of the received metrics data point form a key-value pair. The key-value pair is associated with the received metrics data point.

Abstract: Analyzing log data, such as security log data and machine data, is disclosed. A baseline is built for a set of machine data. The baseline is built at least in part by determining a plurality of signature profiles for a plurality of respective time slices. An occurrence of an anomaly associated with the source of the machine data is determined. The occurrence is determined at least in part by determining that received machine data does not conform to the baseline within a threshold.

Abstract: Clustering structured log data by key schema includes receiving a raw log message. At least a portion of the raw log message comprises structured machine data including a set of key-value pairs. It further includes receiving a map of keys to values. It further includes using the received map of keys to values to determine a key schema of the structured machine data. The key schema is associated with a corresponding cluster. It further includes associating the raw log message with the cluster corresponding to the determined key schema.

Abstract: A processing device receives a first query comprising a first field value and a first time period. The processing device performs a first search of a data store to identify a first plurality of events having the first time period and at least one field that comprises the first field value. The processing device generates a first search object comprising the first field value. The processing device generates a search event comprising the first field value and a reference to the first search object. An event entry for the first search event is then written to the data store. Future searches may return both the first search event and other events.

Abstract: A new cybersecurity incident is registered at a security incident response platform. At a playbook generation system, details are received of the new cybersecurity incident from the security incident response platform. At least some of the details correspond to a set of features of the new cybersecurity incident. A set or subset of nearest neighbors of the new cybersecurity incident is localized in a feature space. The nearest neighbors of the new cybersecurity incident are other cybersecurity incidents having a distance from the new cybersecurity incident within the feature space that is defined by differences in features of the nearest neighbors with respect to the set of features of the new cybersecurity incident. A custom playbook is created for responding to the new cybersecurity incident having prescriptive procedures based on occurrences of prescriptive procedures previously employed in response to the nearest neighbor cybersecurity incidents.

Abstract: A cybersecurity incident is registered at a security incident response platform. At a playbook generation system, details are received of the cybersecurity incident from the security incident response platform. At least some of the details correspond to a set of features of the cybersecurity incident. A set or subset of nearest neighbors of the cybersecurity incident is localized in a feature space. The nearest neighbors of the cybersecurity incident are other cybersecurity incidents having a distance from the cybersecurity incident within the feature space that is defined by differences in features of the nearest neighbors with respect to the set of features of the cybersecurity incident. A playbook is created for responding to the cybersecurity incident having prescriptive procedures based on occurrences of prescriptive procedures previously employed in response to the nearest neighbor cybersecurity incidents.

Abstract: A method and apparatus for controlling digital evidence comprising creating a case record comprising information about an investigative case, electronically storing at least one piece of digital evidence into memory, and associating the stored at least one piece of evidence with the case record.

Abstract: Clustering structured log data by key-values includes receiving, via a user interface, a request to apply an operator to cluster a set of raw log messages according to values for a set of keys associated with the request. At least a portion of each raw log message comprises structured machine data including a set of key-value pairs. It further includes receiving a raw log message in the set of raw log messages. It further includes determining whether to include the raw log message in a cluster based at least in part on an evaluation of values in the structured machine data of the raw log message for the set of keys associated with the request. The cluster is included in a plurality of clusters. Each cluster in the plurality is associated with a different combination of values for the set of keys associated with the request. It further includes providing, via the user interface, information associated with the cluster.

Abstract: Logs to metrics synthesis includes receiving a log message. It further includes translating the log message into a metrics data point comprising a timestamp, a metric name, a metric value, and a set of metadata key-value pairs. It further includes determining a time series in which to insert the metrics data point into which the log message was translated. It further includes inserting the metrics data point into the determined time series. It further includes updating a metadata catalog based at least in part on the metrics data point.

Abstract: A method includes defining a set of context types; defining a set of source types, each comprising context types; defining, for each source type, and for each context type included in the events from data sources having the source type, a context definition comprising a set of fields, in events from the data sources, that are associated with the context type; receiving a query comprising a first field value and a time period; retrieving a plurality of events that include the first field value and the time period; for each retrieved event, and for each context definition defined for a source type and a context type of a data source from which the retrieved event originated, determining field values of fields in the set of fields of the context definition; aggregating, for each context type, determined field values from the events; and generating an output.

Abstract: Key name synthesis is disclosed. A metrics data point is received. Based at least in part on a translation statement, at least a portion of the received metrics data point is associated with a key specified by the translation statement such that the specified key and the associated at least portion of the received metrics data point form a key-value pair. The key-value pair is associated with the received metrics data point.

Abstract: A new cybersecurity incident is registered at a security incident response platform. At a playbook generation system, details are received of the new cybersecurity incident from the security incident response platform. At least some of the details correspond to a set of features of the new cybersecurity incident. A set or subset of nearest neighbors of the new cybersecurity incident is localized in a feature space. The nearest neighbors of the new cybersecurity incident are other cybersecurity incidents having a distance from the new cybersecurity incident within the feature space that is defined by differences in features of the nearest neighbors with respect to the set of features of the new cybersecurity incident. A custom playbook is created for responding to the new cybersecurity incident.

Abstract: A method and apparatus for controlling digital evidence comprising creating a case record comprising information about an investigative case, electronically storing at least one piece of digital evidence into memory, and associating the stored at least one piece of evidence with the case record.

Abstract: Data enrichment and augmentation is disclosed. Machine data comprising at least one of a log message and a metrics data point is received. The received machine data comprises an identifier of an instance of a virtual machine. Based at least in part on the identifier of the instance of the virtual machine, a query for tags associated with the instance of the virtual machine is performed. At least one key-value pair is generated based at least in part on tags received in response to the query performed based at least in part on the identifier of the instance of the virtual machine. The received machine data is augmented with the at least one key-value pair generated based at least in part on the tags received in response to the query based at least in part on the identifier of the instance of the virtual machine.

Abstract: Automatic partitioning is disclosed. A set of previously run queries is obtained. The set of previously run queries is analyzed to determine one or more query fragments from the set of previously run queries. One or more partitions are generated at least in part by using the obtained query fragments.

Abstract: A processing device receives a first query comprising a first field value and a first time period. The processing device performs a first search of a data store to identify a first plurality of events having the first time period and at least one field that comprises the first field value. The processing device generates a first search object comprising the first field value. The first search object may be a data structure, file or data record, and is stored in the data store. The processing device generates a search event comprising the first field value and a reference to the first search object. An event entry for the first search event is then written to the data store. Future searches may return both the first search event and other events, as well as search objects.

Abstract: Analyzing log data, such as security log data and machine data, is disclosed. A baseline is built for a set of machine data. The baseline is built at least in part by determining a plurality of signature profiles for a plurality of respective time slices. An occurrence of an anomaly associated with the source of the machine data is determined. The occurrence is determined at least in part by determining that received machine data does not conform to the baseline within a threshold.

Abstract: Single-click delta analysis is disclosed. A user query of status information collected from one or more monitored devices is received from a user. In response to receiving an indication from the user to determine a variance between different portions of the collected status information, a target query and a baseline query are generated using the user query. The generated target query and the generated baseline query are performed, respectively, against data in a data store including the status information collected from the one or more monitored devices. A target set of status information results and a baseline set of status information results are obtained in response to performing, respectively, the generated target query and the generated baseline query. The obtained target and baseline sets of results are combined. Output indicative of a variance between the target and baseline sets of status information results is provided based at least in part on the combining.