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    Machine learning detection of network attacks using traffic and log information
    3.
    发明申请
    Machine learning detection of network attacks using traffic and log information 有权

    公开(公告)号：US20220263842A1

    公开(公告)日：2022-08-18

    申请号：US17571342

    申请日：2022-01-07

    申请人： Ciena Corporation

    发明人： Zhiyan Chen ,  Murat Simsek ,  Burak Kantarci ,  Petar Djukic ,  James P'ford't Carnes, III ,  Mehran Bagheri ,  Jinxin Liu ,  Yu Shen

    IPC分类号： H04L9/40 ,  G06N3/08

    摘要： Systems and methods for detecting intrusions, attacks, and sub-attacks launched against a network under observations are provided. A method, according to one implementation, includes obtaining network traffic information regarding data traffic in a network under observation and obtaining system log information regarding operations of the network under observation. The method further includes the step of inserting the network traffic information and system log information into one or more analysis procedures, where each analysis procedure is configured to detect a respective sub-attack of a multi-stage attack to which the network under observation is susceptible. Also, the method includes the step of combining the outputs of the one or more analysis procedures to detect whether one or more sub-attacks have been launched against the network under observation. In response to detecting that one or more sub-attacks have been launched, the methods include the step of determining the type of the one or more sub-attacks.

    Forecasting time-series data in a network environment
    5.
    发明申请
    Forecasting time-series data in a network environment 有权

    公开(公告)号：US20210150305A1

    公开(公告)日：2021-05-20

    申请号：US16687902

    申请日：2019-11-19

    申请人： Ciena Corporation

    发明人： Maryam Amiri ,  Petar Djukic ,  Todd Morris

    IPC分类号： G06N3/02 ,  G06N5/02 ,  H04L12/24

    摘要： Systems, methods, and computer-readable medium for forecasting a time-series are provided. In one implementation, a method is configured to include a step of providing a time-series to a neural network including one or more branches for processing one or more portions of the time-series. In each of the one or more branches, the method includes separating the respective portion of the time-series into individual portions and applying each portion to a respective sub-branch of a plurality of sub-branches of the one or more branches. The method also includes generating forecasting coefficients for each output time point in each of the respective sub-branches and providing a forecast of the time-series based at least on the forecasting coefficients.

    Autonomic resource partitions for adaptive networks
    7.
    发明授权
    Autonomic resource partitions for adaptive networks 有权

    公开(公告)号：US11153229B2

    公开(公告)日：2021-10-19

    申请号：US16251394

    申请日：2019-01-18

    申请人： Ciena Corporation

    发明人： Petar Djukic ,  Todd Morris ,  Emil Janulewicz ,  David Jordan Krauss ,  Kaniz Mahdi ,  Paul Littlewood

    IPC分类号： H04L12/911 ,  H04L12/24 ,  G06N3/08 ,  G06F9/50 ,  H04L12/927 ,  H04L12/917

    摘要： System and methods for autonomous resource partitioning in a network include a resource controller configured to provision resources which are any of virtual resources and physical resources in one or more layers in the network and monitor availability of the resources in the network; a resource manager configured to determine the any of virtual resources and physical resources as required for Quality of Service (QoS) in the network; a resource broker configured to advertise and assign resource requests to corresponding resources; and a partition manager configured to track the utilization of the resources provided by the one or more layers and to adjust resource usage of the resources in negotiation with the resource broker to minimize a cost of implementation.

    Forecasting routines utilizing a mixer to combine Deep Neural Network (DNN) forecasts of multi-variate time-series datasets
    8.
    发明申请
    Forecasting routines utilizing a mixer to combine Deep Neural Network (DNN) forecasts of multi-variate time-series datasets 有权

    公开(公告)号：US20210303969A1

    公开(公告)日：2021-09-30

    申请号：US16833781

    申请日：2020-03-30

    申请人： Ciena Corporation

    发明人： Maryam Amiri ,  Petar Djukic ,  Todd Morris

    IPC分类号： G06N3/04 ,  H04B10/07 ,  G06N3/08

    摘要： Deep Neural Networks (DNNs) for forecasting future data are provided. In one embodiment, a non-transitory computer-readable medium is configured to store computer logic having instructions that, when executed, cause one or more processing devices to receive, at each of a plurality of Deep Neural Network (DNN) forecasters, an input corresponding to a time-series dataset of a plurality of input time-series datasets. The instructions further cause the one or more processing devices to produce, from each of the plurality of DNN forecasters, a forecast output and provide the forecast output from each of the plurality of DNN forecasters to a DNN mixer for combining the forecast outputs to produce one or more output time-series datasets.