Abstract: A network connection between a server group of a data intake and query system and each of one or more source network nodes is established. The server group includes an indexer server and a model management server. Source data at the server group is received from at least one of the one or more source network nodes via the respective network connections and transformed, by the indexer server, to timestamped entries of machine data. A model management server detects data constraints for a security model. The data constraints include a data element used by the security model and an availability requirement set, the availability requirement set defining when the data element is available. Using the timestamped entries, the data constraints are validated to obtain a validation result, where validating the data constraints includes determining whether the timestamped entries satisfy the availability requirement set for the data element.

Abstract: A deployment manager executing in a distributed computing environment generates a user behavior analytics (UBA) deployment to process structured event data. The deployment manager configures a streaming cluster to perform streaming processing on real-time data and configures a batch cluster to perform batch processing on aggregated data. A configuration manager executing in the distributed computing environment interoperates with the deployment manager to update the UBA deployment with user-provided code and configurations that define streaming and batch models, among other things. In this manner, the deployment manager provides a scalable UBA deployment that can be customized, via the configuration manager, by a user.

Abstract: A deployment manager executing in a distributed computing environment generates a user behavior analytics (UBA) deployment to process structured event data. The deployment manager configures a streaming cluster to perform streaming processing on real-time data and configures a batch cluster to perform batch processing on aggregated data. A configuration manager executing in the distributed computing environment interoperates with the deployment manager to update the UBA deployment with user-provided code and configurations that define streaming and batch models, among other things. In this manner, the deployment manager provides a scalable UBA deployment that can be customized, via the configuration manager, by a user.

Abstract: A method for analyzing a binary-based application protocol of a network. The method includes obtaining conversations from the network, extracting content of a candidate field from a message in each conversation, calculating a randomness measure of the content to represent a level of randomness of the content across all conversation, calculating a correlation measure of the content to represent a level of correlation, across all of conversations, between the content and an attribute of a corresponding conversation where the message containing the candidate field is located, and selecting, based on the randomness measure and the correlation measure, and using a pre-determined field selection criterion, the candidate offset from a set of candidate offsets as the offset defined by the protocol.

Abstract: The disclosed embodiments include a method performed by a computer system. The method includes forming groups of traffic, where each group includes a subset of detected connection requests. The method further includes determining a periodicity of connection requests for each group, identifying a particular group based on whether the periodicity of connection requests of the particular group satisfies a periodicity criterion, determining a frequency of the particular group in the traffic, and identifying the particular group as an anomaly based on whether the frequency of the particular group satisfies a frequency criterion.

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: A method for analyzing a binary-based application protocol of a network. The method includes obtaining conversations from the network, extracting content of a candidate field from a message in each conversation, calculating a randomness measure of the content to represent a level of randomness of the content across all conversation, calculating a correlation measure of the content to represent a level of correlation, across all of conversations, between the content and an attribute of a corresponding conversation where the message containing the candidate field is located, and selecting, based on the randomness measure and the correlation measure, and using a pre-determined field selection criterion, the candidate offset from a set of candidate offsets as the offset defined by the protocol.

Abstract: A method for analyzing a binary-based application protocol of a network. The method includes obtaining conversations from the network, extracting content of a candidate field from a message in each conversation, calculating a randomness measure of the content to represent a level of randomness of the content across all conversation, calculating a correlation measure of the content to represent a level of correlation, across all of conversations, between the content and an attribute of a corresponding conversation where the message containing the candidate field is located, and selecting, based on the randomness measure and the correlation measure, and using a pre-determined field selection criterion, the candidate offset from a set of candidate offsets as the offset defined by the protocol.

Abstract: A method for analyzing an application protocol of a network. The method includes extracting non-alphanumeric tokens from conversations of the network, selecting frequently occurring non-alphanumeric token as a field delimiter candidate for dividing each conversation into a slice-set, analyzing slice-sets of the conversations to determine a statistical measure of matched slices for each conversation, and -o determine a field delimiter candidate score by aggregating the statistical measure of matched slices for all conversations, and selecting the non-alphanumeric token as the field delimiter of the protocol based on the field delimiter candidate score associated with the non-alphanumeric token.

Abstract: A method for profiling user activity in a mobile network, including extracting user identifiers from application sessions identified from a mobile network, analyzing the application sessions to determine session blocks based on shared IP address and a minimum separation time threshold, extracting a traffic marker from the session blocks based on a user identifier, identifying a first portion of the session blocks based on the user identifier, wherein the first portion is associated with first mobile network activities of a user identified by the user identifier, identifying a second portion of the session blocks based on the traffic marker, wherein the second portion is associated with second mobile network activities of the user, and analyzing the first portion and the second portion to determine a measure of a mobile network activity of the user.

Abstract: Embodiments of the invention provide a method, system, and computer readable medium for classifying network traffic based on application signatures generated during a training phase using a modified subspace clustering scheme based on feature vectors extracted from network flows in a training set generated by a particular application and applying the signatures to a new feature vector extracted in real-time from current network data. The newly extracted feature vector is projected into the subspaces and compared with the signatures.

Abstract: A method for profiling network traffic of a network, including defining a set of features each corresponding to a set of pre-determined bit positions for selecting a pre-determined number of data bits from each flow in a flow set generated by a network application to form a feature value assigned to the feature for the each flow, identifying the feature as a deterministic feature based on a frequency of occurrence of the feature value, extracting a set of paths from the flow set based on a number of deterministic features, generating a state machine based on the set of paths, and analyzing a new flow associated with a server in the network to determine the server as executing the network application.