Machine learning-based multitenant server application dependency mapping system

    公开(公告)号:US12052146B2

    公开(公告)日:2024-07-30

    申请号:US18061834

    申请日:2022-12-05

    摘要: A multitenant server application dependency mapping system maps data flows through multitenant infrastructure components through the use of a machine learning model framework that continually learns data flow patterns across the enterprise network and predicts the state of any given server. The multitenant server application dependency mapping system treats the network architecture as a whole and collects data accordingly, and uses that data to compute state probabilities conditioned upon both a point in time (and the observed prior states retrieved from the historical telemetry data. This provides a way to predict the likelihood of observing a tenant state being occupied, while also accounting for variations among the activity levels of various application. To forecast future states of all infrastructure components, the transition probabilities from tenant state to tenant state are then computed through time and used as inputs to the model to provide an accurate reconstruction of the data flows through all multitenant infrastructure components.

    Dynamic medium switching for hybrid networks

    公开(公告)号:US12015553B2

    公开(公告)日:2024-06-18

    申请号:US17522812

    申请日:2021-11-09

    摘要: A method and apparatus for dynamic medium switching in a hybrid network. A method for packet transmission by a combo device includes maintaining a wireless network confidence rating value that is indicative of packet transfer reliability of a wireless network accessed by the device. A wired network confidence rating value that is indicative of packet transfer reliability of a wired network accessed by the device is also maintained. One of the wireless network and the wired network to be used for initial transmission of the data packet is selected based on which of the wireless confidence rating value and the wired confidence rating value is indicative of a higher likelihood of the packet being successfully transmitted. The packet is routed to be transmitted via the selected network.

    Automated manipulation and monitoring of embeddable browsers

    公开(公告)号:US11968104B2

    公开(公告)日:2024-04-23

    申请号:US18160507

    申请日:2023-01-27

    申请人: PayPal, Inc.

    摘要: Techniques are disclosed relating to monitoring network traffic of an embeddable browser displayed by an application executing on a mobile computing device. In some embodiments, a first layer of the application manipulates one or more user interface elements displayed in the embeddable browser. The first layer of the application then detects network requests made by one or more application programming interfaces (APIs) executed by the embeddable browser in response to the manipulating. In some embodiments, the first layer sends to a second layer of the application results of observing network requests. In some embodiments, the second layer of the application displays, in real-time, information corresponding to the results of observing network requests. The disclosed techniques for monitoring activity on an embeddable browser included in mobile applications despite mobile security restrictions may advantageously reduce or remove wait times associated with manipulating and observing content of the embeddable browser.

    Analysis and mitigation of network security risks

    公开(公告)号:US11949702B1

    公开(公告)日:2024-04-02

    申请号:US18052030

    申请日:2022-11-02

    申请人: Splunk Inc.

    IPC分类号: H04L12/00 H04L9/40 H04L65/61

    CPC分类号: H04L63/1425 H04L65/61

    摘要: A method comprises acquiring anomaly data including a plurality of anomalies detected from streaming data, wherein each of the anomalies relates to an entity on or associated with a computer network. The method determines a risk score of each of the anomalies, and adjusts the risk score of an anomaly according to a set of factors. The method further determines, for each of a plurality of sliding time windows of different lengths, an entity score of the entity in relation to the sliding time window, based on an aggregation of risk scores of all anomalies related to the entity that were detected within the sliding time window, where the entity score corresponds to a risk level associated with the entity. An action to prevent the entity from performing an operation can be determined and caused to occur based on the entity score.