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    Binary search-based approach in routing-metric agnostic topologies for node selection to enable effective learning machine mechanisms
    63.
    发明授权
    Binary search-based approach in routing-metric agnostic topologies for node selection to enable effective learning machine mechanisms 有权
    用于节点选择的路由 - 度量不可知拓扑中的基于二进制搜索的方法，以实现有效的学习机制

    公开(公告)号：US09544220B2

    公开(公告)日：2017-01-10

    申请号：US13946268

    申请日：2013-07-19

    Applicant: Cisco Technology, Inc.

    Inventor： Sukrit Dasgupta ,  Jean-Philippe Vasseur ,  Grégory Mermoud ,  Jonathan W. Hui

    IPC: H04L12/751 ,  H04L12/24 ,  H04L12/26 ,  H04L12/703

    CPC classification number: H04L45/08 ,  H04L41/0677 ,  H04L41/5009 ,  H04L43/106 ,  H04L45/28

    Abstract: In one embodiment, nodes are polled in a network for Quality of Service (QoS) measurements, and a QoS anomaly that affects a plurality of potentially faulty nodes is detected based on the QoS measurements. A path, which traverses the plurality of potentially faulty nodes, is then computed from a first endpoint to a second endpoint. Also, a median node that is located at a point along the path between the first endpoint and the second endpoint is computed. Time-stamped packets are received from the median node, and the first endpoint and the second endpoint of the path are updated based on the received time-stamped packets, such that an amount of potentially faulty nodes is reduced. Then, the faulty node is identified from a reduced amount of potentially faulty nodes.

    Abstract translation: 在一个实施例中，在用于服务质量（QoS）测量的网络中轮询节点，并且基于QoS测量来检测影响多个潜在故障节点的QoS异常。 然后，从第一端点到第二端点计算遍历多个潜在故障节点的路径。 此外，计算位于沿着第一端点和第二端点之间的路径的点处的中间节点。 从中间节点接收时间戳的分组，并且基于接收的时间戳分组来更新路径的第一端点和第二端点，使得可能故障节点的量减少。 然后，从减少量的潜在故障节点识别故障节点。

    Distributed predictive routing using delay predictability measurements
    64.
    发明授权
    Distributed predictive routing using delay predictability measurements 有权
    使用延迟可预测性测量的分布式预测路由

    公开(公告)号：US09525617B2

    公开(公告)日：2016-12-20

    申请号：US14268627

    申请日：2014-05-02

    Applicant: Cisco Technology, Inc.

    Inventor： Jean-Philippe Vasseur ,  Grégory Mermoud ,  Diane Bouchacourt

    IPC: H04L12/721 ,  H04L12/727 ,  H04L12/26

    CPC classification number: H04L45/124 ,  H04L43/0852 ,  H04L45/121 ,  H04L45/123 ,  H04L45/44

    Abstract: In one embodiment, a method is disclosed in which a device receives delay information for a communication segment in a network. The device determines a predictability measurement for delays along the segment using the received delay information. The predictability measurement is advertised to one or more devices in the network and used as a routing constraint to select a routing path in the network.

    Abstract translation: 在一个实施例中，公开了一种方法，其中设备接收网络中的通信段的延迟信息。 该设备使用接收到的延迟信息确定沿着该段的延迟的可预测性测量。 将可预测性测量通告给网络中的一个或多个设备，并用作路由约束来选择网络中的路由路径。

    FINGERPRINT MERGING AND RISK LEVEL EVALUATION FOR NETWORK ANOMALY DETECTION
    65.
    发明申请
    FINGERPRINT MERGING AND RISK LEVEL EVALUATION FOR NETWORK ANOMALY DETECTION 审中-公开
    网络异常检测的指纹合并和风险等级评估

    公开(公告)号：US20160352765A1

    公开(公告)日：2016-12-01

    申请号：US15072526

    申请日：2016-03-17

    Applicant: Cisco Technology, Inc.

    Inventor： Grégory Mermoud ,  Jean-Philippe Vasseur ,  Yannick Weibel

    IPC: H04L29/06

    CPC classification number: H04L63/1425 ,  H04L63/1416 ,  H04L63/145 ,  H04L63/1458

    Abstract: In one embodiment, a device in a network receives fingerprints of two or more network anomalies detected in the network by different anomaly detectors. Each fingerprint comprises a hash of tags that describe a detected anomaly. The device associates the fingerprints with network records captured within a timeframe in which the two or more network anomalies were detected. The device compares the fingerprints associated with the network records to determine that the two or more detected anomalies are part of a singular anomaly event. The device generates a notification regarding the singular anomaly event, wherein the notification includes those of the fingerprints that are associated with the singular anomaly event.

    Abstract translation: 在一个实施例中，网络中的设备由不同的异常检测器接收在网络中检测到的两个或多个网络异常的指纹。 每个指纹包括描述检测到的异常的标签的散列。 该设备将指纹与在其中检测到两个或多个网络异常的时间范围内捕获的网络记录相关联。 设备将与网络记录相关联的指纹进行比较，以确定两个或多个检测到的异常是单个异常事件的一部分。 设备生成关于奇异异常事件的通知，其中通知包括与单个异常事件相关联的指纹的通知。

    ANOMALY DETECTION USING NETWORK TRAFFIC DATA
    67.
    发明申请
    ANOMALY DETECTION USING NETWORK TRAFFIC DATA 审中-公开
    使用网络流量数据进行异常检测

    公开(公告)号：US20160219070A1

    公开(公告)日：2016-07-28

    申请号：US14989920

    申请日：2016-01-07

    Applicant: Cisco Technology, Inc.

    Inventor： Jean-Philippe Vasseur ,  Grégory Mermoud ,  Laurent Sartran

    IPC: H04L29/06 ,  H04L12/751 ,  G06N99/00 ,  H04L12/707

    CPC classification number: H04L63/1425 ,  G06F21/552 ,  G06N99/005 ,  H04L45/02 ,  H04L45/22 ,  H04L45/306

    Abstract: In one embodiment, a device in a network receives traffic metrics for a plurality of applications in the network. The device populates a feature space for a machine learning-based anomaly detector. The device identifies a missing dataset in the feature space for a particular one of the plurality of applications. The device adjusts how traffic is sent in the network, to capture the missing dataset.

    Abstract translation: 在一个实施例中，网络中的设备接收网络中的多个应用的​​业务量度。 该设备填充基于机器学习的异常检测器的特征空间。 所述设备识别所述多​​个应用中的特定空间的所述特征空间中的丢失数据集。 该设备调整网络中流量的发送方式，以捕获丢失的数据集。

    SELECTIVE AND DYNAMIC APPLICATION-CENTRIC NETWORK MEASUREMENT INFRASTRUCTURE
    69.
    发明申请
    SELECTIVE AND DYNAMIC APPLICATION-CENTRIC NETWORK MEASUREMENT INFRASTRUCTURE 有权
    选择性和动态应用中心网络测量基础设施

    公开(公告)号：US20160028608A1

    公开(公告)日：2016-01-28

    申请号：US14591072

    申请日：2015-01-07

    Applicant: Cisco Technology, Inc.

    Inventor： Sukrit Dasgupta ,  Jean-Philippe Vasseur ,  Grégory Mermoud

    IPC: H04L12/26

    CPC classification number: H04L43/0894 ,  H04L43/0805 ,  H04L43/0852 ,  H04L43/103 ,  H04L43/16

    Abstract: In one embodiment, a device in a network receives data indicative of traffic characteristics of traffic associated with a particular application. The device identifies one or more paths in the network via which the traffic associated with the particular application was sent, based on the traffic characteristics. The device determines a probing schedule based on the traffic characteristics. The probing schedule simulates the traffic associated with the particular application. The device sends probes along the one or more identified paths according to the determined probing schedule.

    Abstract translation: 在一个实施例中，网络中的设备接收指示与特定应用相关联的业务的业务特性的数据。 该设备基于流量特征识别网络中的一个或多个路径，通过该路径发送与特定应用相关联的流量。 设备根据流量特性确定探测时间表。 探测时间表模拟与特定应用相关的流量。 设备根据确定的探测时间表沿着一个或多个识别的路径发送探测。

    Hierarchical hybrid batch-incremental learning
    70.
    发明申请
    Hierarchical hybrid batch-incremental learning 有权
    分层混合批量增量学习

    公开(公告)号：US20150332165A1

    公开(公告)日：2015-11-19

    申请号：US14120371

    申请日：2014-05-14

    Applicant: Cisco Technology, Inc.

    Inventor： Grégory Mermoud ,  Jean-Philippe Vasseur ,  Diane Bouchacourt

    IPC: G06N99/00 ,  H04L29/08 ,  G06N5/04

    CPC classification number: G06N99/005 ,  G06N5/048 ,  H04L41/147 ,  H04L41/16 ,  H04L43/08 ,  H04L45/02 ,  H04L45/70 ,  H04L47/00 ,  H04L67/10

    Abstract: In one embodiment, a machine learning model for predicting one or more metrics is run in a network which includes a centralized controller device interconnected with a plurality of edge devices. A batch version of the machine learning model that operates in batch mode is hosted at the centralized controller device. Then, an incremental version of the machine learning model that operates in incremental mode is pushed to an edge device of the plurality of edge devices, such that the incremental version of the machine learning model is hosted at the edge device. As a result, the batch version and the incremental version of the machine learning model run in parallel with one another.

    Abstract translation: 在一个实施例中，用于预测一个或多个度量的机器学习模型在包括与多个边缘设备互连的集中式控制器设备的网络中运行。 在批处理模式下运行的批量版本的机器学习模型托管在集中控制器设备上。 然后，以增量模式运行的机器学习模型的增量版本被推送到多个边缘设备的边缘设备，使得机器学习模型的增量版本被托管在边缘设备处。 因此，批量版本和机器学习模型的增量版本彼此并行运行。

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