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    Implementation of a data processing pipeline using assignable resources and pre-configured resources
    441.
    发明授权
    Implementation of a data processing pipeline using assignable resources and pre-configured resources 有权

    公开(公告)号：US12242892B1

    公开(公告)日：2025-03-04

    申请号：US17444157

    申请日：2021-07-30

    Applicant: Splunk Inc.

    Inventor： Ricky Burnett

    IPC: G06F9/50 ,  G06F9/38 ,  G06F9/455 ,  G06F9/48

    Abstract: Systems and methods are described for a implementing a streaming data processing system that includes a pool of pre-configured resources and a pool of dedicated resources. The streaming data processing system can implement a processing pipeline using compute resources. The pool of pre-configured resources can support previews of processing pipelines for a plurality of users and the pool of dedicated resources can support full deployments of processing pipelines for a particular user. The streaming data processing system can implement a preview of a processing pipeline using a pre-configured resource of the pool of pre-configured resources. Further, the streaming data processing system can implement the processing pipeline using a dedicated resource of the pool of dedicated resources. The streaming data processing system can provision the dedicated resource and deploy the processing pipeline using the dedicated resource.

    Anomaly Detection System and Method for Implementing a Data Regularity Check and Adaptive Thresholding
    446.
    发明申请
    Anomaly Detection System and Method for Implementing a Data Regularity Check and Adaptive Thresholding 有权

    公开(公告)号：US20250028618A1

    公开(公告)日：2025-01-23

    申请号：US18222870

    申请日：2023-07-17

    Applicant: Splunk Inc.

    Inventor： Houwu Bai ,  Kristal Curtis ,  William Deaderick ,  Tanner Gilligan ,  Poonam Yadav ,  Om Rajyaguru

    IPC: G06F11/34 ,  G06F11/30 ,  G06F16/23 ,  G06F16/2458

    Abstract: Computerized methodologies are disclosed that are directed to detecting anomalies within a time-series data set. A first aspect of the anomaly detection process includes analyzing the regularity of the data points of the time-series data set and determining whether a data aggregation process is to be performed based on the regularity of the data points, which results in a time-series data set having data points occurring at regular intervals. A seasonality pattern may be determined for the time-series data set, where a silhouette score is computed to measure the quality of the fit of the seasonality pattern to the time-series data. The silhouette score may be compared to a threshold and based on the comparison, the seasonality pattern or a set of heuristics may be utilized in an anomaly detection process. When the seasonality pattern is utilized, the seasonality pattern may be utilized to generate thresholds indicating anomalous behavior.

    Graph-based detection of network security issues
    447.
    发明授权
    Graph-based detection of network security issues 有权

    公开(公告)号：US12206693B1

    公开(公告)日：2025-01-21

    申请号：US17745482

    申请日：2022-05-16

    Applicant: Splunk Inc.

    Inventor： Georgios Apostolopoulos

    IPC: H04L29/06 ,  G06F16/901 ,  H04L9/40

    Abstract: The disclosed techniques relate to a graph-based network security analytic framework to combine multiple sources of information and security knowledge in order to detect risky behaviors and potential threats. In some examples, the input can be anomaly events or simply regular events. The entities associated with the activities can be grouped into smaller time units, e.g., per day. The riskiest days of activity can be found by computing a risk score for each day and according to the features in the day. A graph can be built with links between the time units. The links can also receive scoring based on a number of factors. The resulting graph can be compared with known security knowledge for adjustments. Threats can be detected based on the adjusted risk score for a component (i.e., a group of linked entities) as well as a number of other factors.

    Systems and methods for detecting beaconing communications using machine learning techniques
    449.
    发明授权
    Systems and methods for detecting beaconing communications using machine learning techniques 有权

    公开(公告)号：US12199997B1

    公开(公告)日：2025-01-14

    申请号：US17573335

    申请日：2022-01-11

    Applicant: SPLUNK Inc.

    Inventor： Cui Lin ,  Stanislav Miskovic

    IPC: H04L9/40 ,  G06N20/00 ,  H04L41/16 ,  H04L41/22

    Abstract: A computerized method is disclosed that includes operations of obtaining network traffic data between a source device and a destination device, applying a set of one or more security rules to a plurality of metrics of the network traffic data to obtain a subset of network traffic metrics, applying a first trained machine learning model to the subset of network traffic metrics to generate a feature vector through feature extraction of the subset of network traffic metrics, and evaluate the feature vector for a presence of beaconing and classify the subset of network traffic metrics, and responsive to the classifying of the subset of network traffic metrics, generating a flag for a system administrator. The plurality of metrics include at least one or more of packet size, packet transmission rate, or a ratio of (i) packet size for inbound packets and (ii) packet size for outbound packets.

    Ingest preview of events in a network computing environment
    450.
    发明授权
    Ingest preview of events in a network computing environment 有权

    公开(公告)号：US12197968B1

    公开(公告)日：2025-01-14

    申请号：US17875172

    申请日：2022-07-27

    Applicant: Splunk Inc.

    Inventor： Amritpal Singh Bath ,  Samat Jain ,  Isabelle Park ,  Vishal Patel ,  Siegfried Puchbauer ,  Tingjin Xu

    IPC: G06F15/16 ,  G06F9/54 ,  G06F16/26 ,  G06F16/28

    Abstract: A computing device receives an ingest preview request to preview events to be stored by at least one indexer. Responsive to the ingest preview request, the computing device sends a subscription request to the forwarders. The forwarders receive the subscription request and intercept the events that are being sent to at least one of the indexers. The forwarders then clone matching events to the subscription request and responds to the computing device with the matching events. When the computing device receives the matching events, the computing device adds the matching events to a dispatch directory. The user interface is then populated with events in the dispatch directory.

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