A computer system identifies malicious Uniform Resource Locator (URL) data items from a plurality of unscreened data items that have not been previously identified as associated with malicious URLs. The system can execute a number of pre-filters to identify a subset of URLs in the plurality of data items that are likely to be malicious. A scoring processor can score the subset of URLs based on a plurality of input vectors using a suitable machine learning model. Optionally, the system can execute one or more post-filters on the score data to identify data items of interest. Such data items can be fed back into the system to improve machine learning or can be used to provide a notification that a particular resource within a local network is infected with malicious software.

Malicious software detection in a computing system

There is provided an information processing device including a display section configured to display a first object in a virtual three-dimensional space having a depth direction of a display screen, an operation section configured to acquire an operation for moving the first object in at least the depth direction, and a controller configured to move the first object on the display screen in accordance with the acquired operation, to execute, when a region of the first object overlaps a first overlap determination region, a first process to one or both of the first and second objects, and to execute, when the region of the first object overlaps a second overlap determination region, a second process to one or both of the first and second objects. The first overlap determination region may be a region obtained by extending the second overlap determination region in at least the depth direction.

Information processing device, information processing method, and program

Embodiments of the present disclosure relate to a computer system and interactive user interfaces configured to enable efficient and rapid access to multiple different data sources simultaneously, and by an unskilled user. The unskilled user may provide simple and intuitive search terms to the system, and the system may thereby automatically query multiple related data sources of different types and present results to the user. Data sources in the system may be efficiently interrelated with one another by way of a mathematical graph in which nodes represent data sources and/or portions of data sources (for example, database tables), and edges represent relationships among the data sources and/or portions of data sources. For example, edges may indicate relationships between particular rows and/or columns of various tables. The table graph enables a compact and memory efficient storage of relationships among various disparate data sources.

Systems and user interfaces for dynamic and interactive simultaneous querying of multiple data stores

A service monitoring system receives a selection of key performance indicators (KPIs) that each indicate a different aspect of how a service provided by one or more entities is performing. Each entity of the one or more entities produces machine data or wherein each entity has its operation reflected in machine data not produced by the entity. Each KPI is defined by a different search query that derives one or more values from the machine data pertaining to the one or more entities providing the service, where each of the one or more values is associated with a point in time and representing the aspect of how the service is performing at the associated point in time. For each of the selected KPIs, the service monitoring system derives the one or more values and causes display of a graphical visualization of the derived one or more values for the KPI along a time-based graph lane. The graph lanes for the selected KPIs are parallel to each other and the graphical visualizations in the graph lanes are all calibrated to a same time scale.

Defining a graphical visualization along a time-based graph lane using key performance indicators derived from machine data

Systems and methods are disclosed for associating an entity with a risk score that may indicate a security threat associated with the entity's activity An exemplary method may involve monitoring the activity of a subset of the set of entities (eg, entities included in a watch list) by executing a search query against events indicating the activity of the subset of entities The events may be associated with timestamps and may include machine data Executing the search query may produce search results that pertain to activity of a particular entity from the subset The search results may be evaluated based on a triggering condition corresponding to the statistical baseline When the triggering condition is met, a risk score for the particular entity may be updated The updated risk score may be displayed to a user via a graphical user interface (GUI)

User activity monitoring

A metric value is determined for each event in a set of events that characterizes a computational communication or object. For example, a metric value could include a length of a URL or agent string in the event. A subset criterion is generated, such that metric values within the subset are relatively separated from a population's center (e.g., within a distribution tail). Application of the criterion to metric values produces a subset. A representation of the subset is presented in an interactive dashboard. The representation can include unique values in the subset and counts of corresponding event occurrences. Clients can select particular elements in the representation to cause more detail to be presented with respect to individual events corresponding to specific values in the subset. Thus, clients can use their knowledge system operations and observance of value frequencies and underlying events to identify anomalous metric values and potential security threats.

Investigative and dynamic detection of potential security-threat indicators from events in big data

A system and computer-implemented is provided for displaying a configurable metric relating to an environment in a graphical display along with a value of the metric calculated over a configurable time period. The metric is used to identify events of interest in the environment based on processing real time machine data from one or more sources. The configurable metric is selected and a corresponding value is calculated based on the events of interest over the configurable time period. The value of the metric may be continuously updated in real time based on receiving additional real-time machine data and displayed in a graphical interface as time progresses. Statistical trends in the value of the metric may also be determined over the configurable time period and displayed in the graphical interface as well as an indication if the value of the metric exceeds a configurable threshold value. Further, a selection of one or more thresholds for the value of the metric may be applied and an indication displayed indicating if the threshold(s) have been exceeded.

Key indicators view

A disclosed computer-implemented method includes receiving and indexing the raw data. Indexing includes dividing the raw data into time stamped searchable events that include information relating to computer or network security. Store the indexed data in an indexed data store and extract values from a field in the indexed data using a schema. Search the extracted field values for the security information. Determine a group of security events using the security information. Each security event includes a field value specified by a criteria. Present a graphical interface (GI) including a summary of the group of security events, other summaries of security events, and a remove element (associated with the summary). Receive input corresponding to an interaction of the remove element. Interacting with the remove element causes the summary to be removed from the GI. Update the GI to remove the summary from the GI.

Blacklisting and whitelisting of security-related events

A visualization can include a set of swim lanes, each swim lane representing information about an event type An event type can be specified, eg, as those events having certain keywords and/or having specified value(s) for specified field(s) The swim lane can plot when (within a time range) events of the associated event type occurred Specifically, each such event can be assigned to a bucket having a bucket time matching the event time A swim lane can extend along a timeline axis in the visualization, and the buckets can be positioned at a point along the axis that represents the bucket time Thus, the visualization may indicate whether events were clustered at a point in time Because the visualization can include a plurality of swim lanes, the visualization can further indicate how timing of events of a first type compare to timing of events of a second type

Multi-lane time-synched visualizations of machine data events

One or more processing devices cause display of a service-monitoring dashboard that includes one or more key performance indicator (KPI) widgets. Each KPI widget provides a numerical or graphical representation of one or more values for a corresponding KPI indicating how a service provided by one or more entities is performing at one or more points in time. Each entity of the one or more entities is associated with machine data. A KPI is defined by a search query that derives the one or more values represented by the corresponding KPI widget from the machine data associated with the one or more entities that provide the service whose performance is reflected by the KPI.

Presenting a service-monitoring dashboard using key performance indicators derived from machine data

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