Abstract: Transmission handling of analytics query response includes a search head, in a data intake and query system, receiving a query from an analytics system. The search head distributes at least a portion of the query to at least one indexer for processing the query. The at least one indexer transmits, bypassing the search head, and to the analytics system, events matching the query. The search head receives from the at least one indexer, data regarding the events, and sends the data regarding the events to the analytics system.

Abstract: Transmission handling of analytics query response includes a search head, in a data intake and query system, receiving a query from an analytics system. The search head distributes at least a portion of the query to at least one indexer for processing the query. The at least one indexer transmits, bypassing the search head, and to the analytics system, events matching the query. The search head receives from the at least one indexer, data regarding the events, and sends the data regarding the events to the analytics system.

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: An address book API according to the present invention includes an abstracted API and one or more address book specific adapters. The abstracted API provides functionalities common to a plurality of address book programs. The abstracted API is abstract enough to readily interface with various address books. Each adapter provides for address book program specific implementation of functionalities.

Abstract: Methods and systems thereof for storing and retrieving device-dependent attributes on a portal server are described. Attributes are stored in a device-dependent fashion. When a request for an attribute is received, the type of device used to generate the request is identified. The attribute suitable for the type of device is selected. Accordingly, device-dependent attributes are readily stored and retrieved on the portal server. As a result, the portal server can quickly and efficiently provide services and other types of support for a wide variety of client devices having different characteristics.

Abstract: A method and system for customizable client aware content aggregation and rendering in a portal server. The method includes step of servicing a request for content using at least one of a plurality of channels. To service the request, a first file path is accessed. The first file path points to generic non-customized information for a client device. A second of file path is accessed to service the request, wherein the second file path points to customized information, and wherein the second file path is accessed subsequent to the first file path. Aggregated content from a plurality channels is processed using a rendering engine, wherein the rendering engine is configured to output the aggregated content in a markup language tailored for the client device. The aggregated content is subsequently output in the second markup language to the client device.

Abstract: Methods and systems thereof for storing, reading and writing wireless client device profiles are described. A node is created for each device and the properties of the client devices are stored as attributes of a respective node. By storing the profiles in this manner, the reading of a device property reduces to fetching an attribute of the appropriate node, and the writing of a device property is reduced to modifying an attribute of the appropriate node. Parsing and validation of device profiles are eliminated, improving performance. Moreover, the profiles are managed by a user-friendly interface.

Abstract: A wireless portal system having a wireless portal server with a hierarchical client detection mechanism. The hierarchical client detection mechanism includes logic for identifying client wireless devices connecting to the wireless portal server by using a hierarchical search path of predefined client profile information stored in the wireless portal server. In searching for client profile information, the client detection logic first looks for an exact match for a connecting device and if that fails, attempts to find an approximate match of the predefined profile information for the connecting device. A failure in finding either an exact or approximate match results in the client detection logic looking for a match within the profile information of a class of devices similar to the connecting client wireless device.