Abstract: The present disclosure is directed to systems, apparatuses and methods for mitigating cyber-attacks. For example, the method includes receiving, from one or more network devices tracking activity on a network, one or more data streams associated with a respective one of the one or more network devices, identifying a security alert from the one or more data streams, the security alert including metadata context describing the network device from the one or more network devices that originated the security alert, analyzing the metadata context to generate a metadata context score. When the security alert is determined to be a security threat event, classifying a type of the security threat event based on the related activity score and the metadata context, and outputting a recommended mitigation course of action based on the classified type of the security threat event.

Abstract: Systems, methods, and computer-readable storage media for quantifying meeting effectiveness for an individual. A system configured as disclosed herein uses data from multiple meetings in which a user participated to create a user profile for the user. The system then receives data related to a new meeting in which the user participated, processes the new meeting data into segments using natural language processing, tags the resulting segments based on contexts, and compares the tagged segments to the user profile to generate a meeting effectiveness score for the new meeting which is specific to the user. The system can use machine learning to iteratively improve an ability of the system to generate the tagged segments using historical meeting data and updating that historical meeting data with each iteration of scoring a meeting's effectiveness.

Abstract: The present disclosure is directed to systems, apparatuses and methods for mitigating cyber-attacks. For example, the method includes receiving, from one or more network devices tracking activity on a network, one or more data streams associated with a respective one of the one or more network devices, identifying a security alert from the one or more data streams, the security alert including metadata context describing the network device from the one or more network devices that originated the security alert, analyzing the metadata context to generate a metadata context score. When the security alert is determined to be a security threat event, classifying a type of the security threat event based on the related activity score and the metadata context, and outputting a recommended mitigation course of action based on the classified type of the security threat event.

Abstract: Systems, methods, and computer-readable storage media for quantifying meeting effectiveness for an individual. A system configured as disclosed herein uses data from multiple meetings in which a user participated to create a user profile for the user. The system then receives data related to a new meeting in which the user participated, processes the new meeting data into segments using natural language processing, tags the resulting segments based on contexts, and compares the tagged segments to the user profile to generate a meeting effectiveness score for the new meeting which is specific to the user. The system can use machine learning to iteratively improve an ability of the system to generate the tagged segments using historical meeting data and updating that historical meeting data with each iteration of scoring a meeting's effectiveness.

Abstract: Systems, methods, and computer-readable storage media for quantifying meeting effectiveness for a group of participants and reducing redundancies in future meetings. The system receives electronic transcripts for meetings and executes natural language processing on the respective transcripts. The system identifies keywords in the resulting, parsed transcripts, tags the transcript with metadata about the keywords, and generates meeting profiles for the respective meetings. The system then compares the generated meeting profiles, identifies points of redundancy between the meetings from that comparison, and modifies a future meeting based on the identified redundancies.

Abstract: The present disclosure is directed to systems, apparatuses and methods for mitigating cyber-attacks. For example, the method includes receiving, from one or more network devices tracking activity on a network, one or more data streams associated with a respective one of the one or more network devices, identifying a security alert from the one or more data streams, the security alert including metadata context describing the network device from the one or more network devices that originated the security alert, analyzing the metadata context to generate a metadata context score. When the security alert is determined to be a security threat event, classifying a type of the security threat event based on the related activity score and the metadata context, and outputting a recommended mitigation course of action based on the classified type of the security threat event.

Abstract: The present disclosure is directed to systems, apparatuses and methods for mitigating cyber-attacks. For example, the method includes receiving, from one or more network devices tracking activity on a network, one or more data streams associated with a respective one of the one or more network devices, identifying a security alert from the one or more data streams, the security alert including metadata context describing the network device from the one or more network devices that originated the security alert, analyzing the metadata context to generate a metadata context score. When the security alert is determined to be a security threat event, classifying a type of the security threat event based on the related activity score and the metadata context, and outputting a recommended mitigation course of action based on the classified type of the security threat event.

Abstract: Exemplary embodiments may provide an automated approach for processing an input data sample to yield a set of object classes, a parser and one or more unit tests for input data that is to be integrated into a data lake. The objects may be readily queried and, in some instances, may be Plain Old Java Objects (POJO's). The exemplary embodiments may process an input data sample to better understand the format of the input data. The input sample may be processed to identify entities, such as records, objects or the like, in the input data sample. The input data sample may be processed on a line by line basis to identify fields in the entities. Once the format of the input data is determined from the input data sample, a parser may be generated to parse the input data.

Abstract: A method and system for processing network data and displaying the results using a customizable user interface are disclosed.

Abstract: A method and system for processing network data and displaying the results using a customizable user interface are disclosed.