Abstract:
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.
Abstract:
Methods and apparatus consistent with the invention provide the ability to organize, index, search, and present time series data based on searches. Time series data are sequences of time stamped records occurring in one or more usually continuous streams, representing some type of activity. In one embodiment, time series data is organized into discrete events with normalized time stamps and the events are indexed by time and keyword. A search is received and relevant event information is retrieved based in whole or in part on the time indexing mechanism, keyword indexing mechanism, or statistical indices calculated at the time of the search.
Abstract:
A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
Abstract:
A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
Abstract:
A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
Abstract:
A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
Abstract:
A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
Abstract:
A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
Abstract:
One or more processing devices derive a value for each of a plurality of key performance indicators (KPIs). Each KPI indicates a different aspect of how the same service provided by one or more entities is performing at a point in time. Each KPI is defined by a search query that derives the value for that KPI from machine data associated with the one or more entities that provide the same service. The one or more processing devices calculate a value for an aggregate KPI for the same service from the values for each of the plurality of KPIs.
Abstract:
Techniques and mechanisms are disclosed that enable network security analysts and other users to efficiently conduct network security investigations and to produce useful representations of investigation results. As used herein, a network security investigation generally refers to an analysis by an analyst (or team of analysts) of one or more detected network events that may pose internal and/or external threats to a computer network under management. A network security application provides various interfaces that enable users to create investigation timelines, where the investigation timelines display a collection of events related to a particular network security investigation. A network security application further provides functionality to monitor and log user interactions with the network security application, where particular logged user interactions may also be added to one or more investigation timelines.