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    SCHEDULING A NETWORK ATTACK TO TRAIN A MACHINE LEARNING MODEL
    31.
    发明申请
    SCHEDULING A NETWORK ATTACK TO TRAIN A MACHINE LEARNING MODEL 审中-公开
    调度网络攻击训练机器学习模型

    公开(公告)号：US20150195145A1

    公开(公告)日：2015-07-09

    申请号：US14164467

    申请日：2014-01-27

    Applicant: Cisco Technology, Inc.

    Inventor： Andrea Di Pietro ,  Jean-Philippe Vasseur ,  Javier Cruz Mota

    IPC: H04L12/24 ,  G06N3/02

    CPC classification number: H04L63/1425 ,  G06N3/02 ,  G06N3/08 ,  G06N3/084 ,  G06N20/00 ,  H04L41/16 ,  H04L43/0876 ,  H04L45/48 ,  H04L47/127 ,  H04L47/2466 ,  H04L47/41

    Abstract: In one embodiment, a device evaluates a set of training data for a machine learning model to identify a missing feature subset in a feature space of the set of training data. The device identifies a plurality of network nodes eligible to initiate an attack on a network to generate the missing feature subset. One or more attack nodes are selected from among the plurality of network nodes. An attack routine is provided to the one or more attack nodes to cause the one or more attack nodes to initiate the attack. An indication that the attack has completed is then received from the one or more attack nodes.

    Abstract translation: 在一个实施例中，设备评估用于机器学习模型的一组训练数据，以识别该组训练数据的特征空间中的丢失特征子集。 该设备识别有资格发起对网络的攻击的多个网络节点以产生丢失的特征子集。 从多个网络节点中选择一个或多个攻击节点。 向一个或多个攻击节点提供攻击程序以使一个或多个攻击节点发起攻击。 然后从一个或多个攻击节点接收到攻击完成的指示。

    ATTACK MITIGATION USING LEARNING MACHINES
    33.
    发明申请
    ATTACK MITIGATION USING LEARNING MACHINES 有权
    使用学习机器进行攻击减轻

    公开(公告)号：US20150188935A1

    公开(公告)日：2015-07-02

    申请号：US14165424

    申请日：2014-01-27

    Applicant: Cisco Technology, Inc.

    Inventor： Jean-Philippe Vasseur ,  Javier Cruz Mota ,  Andrea Di Pietro ,  Jonathan W. Hui

    IPC: H04L29/06 ,  G06N99/00

    CPC classification number: H04L63/1416 ,  G06N99/005 ,  H04K3/226 ,  H04K2203/18 ,  H04L63/1441 ,  H04L63/1458 ,  H04W12/12

    Abstract: In one embodiment, techniques are shown and described relating to attack mitigation using learning machines. A node may receive network traffic data for a computer network, and then predict a probability that one or more nodes are under attack based on the network traffic data. The node may then decide to mitigate a predicted attack by instructing nodes to forward network traffic on an alternative route without altering an existing routing topology of the computer network to reroute network communication around the one or more nodes under attack, and in response, the node may communicate an attack notification message to the one or more nodes under attack.

    Abstract translation: 在一个实施例中，与使用学习机器的攻击缓解有关的技术被示出和描述。 节点可以接收计算机网络的网络流量数据，然后基于网络流量数据预测一个或多个节点受到攻击的概率。 然后，节点可以通过指示节点在替代路由上转发网络流量而不改变计算机网络的现有路由拓扑以重新路由在被攻击的一个或多个节点周围的网络通信，并且响应于节点 可以将攻击通知消息传送给被攻击的一个或多个节点。

    ANALYZING THE IMPACT OF NETWORK EVENTS ACROSS TIME
    35.
    发明申请
    ANALYZING THE IMPACT OF NETWORK EVENTS ACROSS TIME 有权

    公开(公告)号：US20230080544A1

    公开(公告)日：2023-03-16

    申请号：US18058103

    申请日：2022-11-22

    Applicant: Cisco Technology, Inc.

    Inventor： Javier Cruz Mota ,  Erwan Barry Tarik Zerhouni ,  Abhishek Kumar

    IPC: H04L43/067 ,  H04L43/045 ,  H04L43/16

    Abstract: The present technology pertains to a system, method, and non-transitory computer-readable medium for evaluating the impact of network changes. The technology can detect a temporal event, wherein the temporal event is associated with a change in a network configuration, implementation, or utilization; define a first period prior to the temporal event and a second period posterior to the temporal event; and compare network data collected in the first period and network data collected in the second period.

    Technologies for dynamically generating network topology-based and location-based insights
    37.
    发明授权
    Technologies for dynamically generating network topology-based and location-based insights 有权

    公开(公告)号：US11296964B2

    公开(公告)日：2022-04-05

    申请号：US16563472

    申请日：2019-09-06

    Applicant: Cisco Technology, Inc.

    Inventor： Abhishek Kumar ,  Erwan Barry Tarik Zerhouni ,  Javier Cruz Mota

    IPC: H04L12/26 ,  H04L43/067 ,  H04L43/04

    Abstract: Technologies for dynamically generating topology and location based network insights are provided. In some examples, a method can include determining statistical changes in time series data including a series of data points associated with one or more conditions or parameters of a network; determining a period of time corresponding to one or more of the statistical changes in the time series data; obtaining telemetry data corresponding to a segment of the network and one or more time intervals, wherein a respective length of each time interval is based on a length of the period of time corresponding to the one or more of the statistical changes in the time series data; and generating, based on the telemetry data, insights about the segment of the network, the insights identifying a trend or statistical deviation in a behavior of the segment of the network during the one or more time intervals.

    DYNAMIC MACHINE LEARNING ON PREMISE MODEL SELECTION BASED ON ENTITY CLUSTERING AND FEEDBACK
    39.
    发明申请
    DYNAMIC MACHINE LEARNING ON PREMISE MODEL SELECTION BASED ON ENTITY CLUSTERING AND FEEDBACK 有权

    公开(公告)号：US20210056463A1

    公开(公告)日：2021-02-25

    申请号：US16548710

    申请日：2019-08-22

    Applicant: Cisco Technology, Inc.

    Inventor： Erwan Barry Tarik Zerhouni ,  Abhishek Kumar ,  Javier Cruz Mota

    IPC: G06N20/00 ,  G06Q10/06

    Abstract: The disclosed technology relates to a process of providing dynamic machine learning on premise model selection. In particular, a set of machine learned models are generated and provided to an on premise computing device. The machine learned models are generated using a cluster of customer data (e.g. telemetric data) stored on a computing network having different ranges of computational complexity. One of the machine learned models from the set of machine learned models will be selected based on the current available computational resources detected at the on premise computing device. Different machine learned models from the set of machine learned models can then be selected based on changes in the available computational resources and/or customer feedback

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